We are building AI agents and need cheaper inference for long tool-using workflows. What chip platforms should we evaluate?

For long, multi-turn, tool-using agentic workflows, your biggest cost and latency bottlenecks aren't just raw compute—they are memory bandwidth for the KV cache and Time-to-First-Token (TTFT) during prompt re-evaluation/caching . Agents repeatedly read long context histories, dump tool outputs, and re-process state.

When evaluating chip platforms for cost-effective agent inference, you should look beyond raw FLOPs and focus on how the architecture handles memory tiers and cache retention.

1. Hybrid Memory / Reconfigurable Dataflow (Best for Long Context & Agents)

• SambaNova (Reconfigurable Dataflow Units - RDU):
  • Why evaluate: They use a distinct three-tier memory architecture (SRAM + HBM + high-capacity DDR/Flash tiers) specifically designed to keep large prompt/KV caches close and affordable without requiring massive clusters of HBM-heavy GPUs.
  • Agent benefit: Excellent rack-level token efficiency and low TTFT on long-chained agent actions because large context states don't choke the primary memory bandwidth.
• Emerging High-Bandwidth Flash (HBF) / CXL-Attached Tiering:
  • Why evaluate: Server architectures implementing hybrid HBM + high-bandwidth flash or CXL-attached memory expansion.
  • Agent benefit: Drastically lowers the cost-per-gigabyte for massive KV caches compared to stacking expensive high-bandwidth memory (HBM) alone.

2. High-Bandwidth SRAM Architectures (Best for Speed, Tricky for Size)

• Groq (LPU - Language Processing Unit):
  • Why evaluate: Pure SRAM-based deterministic architecture delivering blistering token-generation speeds.
  • Agent benefit: Incredible throughput when the model fits within the SRAM constraints.
  • The Catch: Pure SRAM has tight capacity limits. For long-context tool-using agents that need massive sliding-window or persistent KV caches, you need massive board counts or rack scaling, which impacts power and physical footprint. Evaluate them if your agent steps are short-context/high-frequency, but look closely at multi-rack scaling costs for massive context.

3. The Enterprise Standard (Best for Software & Cache Disaggregation)

• NVIDIA (Hopper H100 / Blackwell B200 / GB200 systems):
  • Why evaluate: While upfront hardware isn't "cheap," the software ecosystem around NVIDIA is unmatched for cost reduction via engineering. Frameworks supporting prefill/decode disaggregation and hierarchical KV caching (like vLLM, TensorRT-LLM, or llm-d integrations) run most maturely here.
  • Agent benefit: You can leverage aggressive prefix caching and vLLM-style paged attention. If multiple agent threads share a system prompt or tool definition blocks, NVIDIA's mature kernel support lets you share KV cache blocks across requests, drastically cutting redundant compute costs.

4. Custom Hyperscaler & AI ASIC Alternatives

• AWS Inferentia2 / Google TPU (v5p / v6e):
  • Why evaluate: If you are deploying natively inside AWS or GCP, their custom inference accelerators offer substantially lower dollar-per-token cloud pricing than standard rented H100/B200 clusters if you lock into their managed serving runtimes.
  • Agent benefit: Google TPUs excel at massive matrix math for very long sequence lengths (large native memory footprints), which suits sprawling agent states if you can containerize your workflow to their stack.

Key Evaluation Criteria for Your Architecture:

To help narrow down the ideal platform, tell me:

• Are you planning to host and manage on-prem/bare-metal hardware or deploy via serverless/cloud APIs?
• What is your expected average context length per agent turn (e.g., <8k tokens vs. 64k+ tokens)?

For long, tool-using agent workflows, I’d evaluate accelerators differently from conventional LLM serving. The important metric is not peak tokens/sec; it’s $/successful agent task at your target latency, because agents alternate between model calls, tool execution, KV-cache reuse, and often relatively small decode bursts.

My shortlist

AWS explicitly positions Trainium around inference economics at scale, while Inferentia is optimized for lower-cost inference. Amazon Web Services Amazon Web Services AMD's current MI355X is particularly interesting because it offers 288 GB HBM3E and 8 TB/s bandwidth per GPU, which can materially change the economics of large/long-context models.

Where I'd put the effort

1. Blackwell — baseline you should beat

Use B200/B300-class NVIDIA systems as the control. Even if another accelerator wins on raw $/token, CUDA, TensorRT-LLM, networking, quantization, speculative decoding, KV-cache tooling, and model availability can make NVIDIA cheaper end-to-end.

Recent benchmarking also illustrates how much serving software can matter: reported GB300 results showed enormous changes in cost/token from software optimization alone.

2. MI355X — probably the most important alternative to benchmark yourself

This is the non-NVIDIA platform I'd put near the top of the evaluation. AMD is now explicitly targeting distributed inference and reports competitive TCO against B200 on disaggregated workloads using SGLang/MoRI. AMD Its large HBM footprint is particularly attractive for agents with long contexts, large models, or lots of concurrent KV cache.

3. Trainium — especially if you're AWS-native

Trainium deserves a serious production test rather than a paper comparison. AWS is explicitly designing the platform around training and inference economics at scale, with Neuron as the software stack.

For an agent company already operating heavily in AWS, the infrastructure/network/storage economics can matter as much as accelerator $/hour.

4. Groq and Cerebras — test these specifically for agent latency

These are interesting because agent workloads are unusually sensitive to decode latency. An agent might do:

reason → tool → reason → tool → reason → tool → final

So shaving seconds off each generation can improve the total workflow much more than maximizing batch throughput.

Cerebras, for example, is explicitly pursuing a Trainium-for-prefill + Cerebras-for-decode architecture with AWS. US Press Center Cerebras also reports very high inference speeds for agentic workloads. Cerebras Groq is similarly positioning its architecture around low-latency inference for agents.

The benchmark I'd actually run

Don't benchmark tokens/sec in isolation. Replay real agent traces and measure:

• $/completed task
• p50/p95 time-to-tool
• p50/p95 time-to-final-answer
• input/output tokens per successful task
• tokens/sec during decode
• prefill latency
• KV-cache hit rate and memory footprint
• concurrency at your target SLA
• failure/retry rate
• utilization during the actual agent trace
• engineering cost to port a new model
• cost when the model/context size changes

I'd construct at least three workloads:

1. Short-loop agent: 3–5 model calls, moderate context.
2. Long-loop agent: 15–30 model/tool iterations with growing context.
3. Heavy-context agent: very large prompt/KV cache with relatively modest output.

That third workload is where HBM capacity/bandwidth and memory economics can overturn a conventional GPU benchmark. Academic work comparing accelerators similarly finds that the optimal platform changes with batch size, sequence length, and model size.

My expected evaluation order

If I were building your infrastructure team, I'd spend the first benchmark budget on:

NVIDIA Blackwell → AMD MI355X → AWS Trainium → Groq → Cerebras → Google TPU → Inferentia2

Then let your actual traces reorder them.

The especially interesting architecture for agents is disaggregated prefill/decode rather than simply "cheapest GPU." AWS/Cerebras are already pursuing exactly this split, and AMD/Cerebras announced a similar architecture in July 2026.

If your agents are doing long contexts + lots of sequential tool calls, I'd put MI355X, Trainium, Groq, and Cerebras unusually high on the test list rather than assuming NVIDIA wins.

For an agent company already operating heavily in AWS, the infrastructure/network/storage economics can matter as much as accelerator $/hour.

4. Groq and Cerebras — test these specifically for agent latency

These are interesting because agent workloads are unusually sensitive to decode latency. An agent might do:

reason → tool → reason → tool → reason → tool → final

So shaving seconds off each generation can improve the total workflow much more than maximizing batch throughput.

For long, tool-using agent workflows, I’d evaluate inference economics at the workflow level, not just $/GPU-hour. Agents amplify decode latency and context/KV-cache costs because one task can trigger dozens of sequential model calls. NVIDIA explicitly calls out tokens/sec/user, tokens/watt, and cost/token as better metrics for these workloads.

Platforms I’d put on the shortlist

The interesting development right now is that agentic inference is creating a distinct hardware regime. NVIDIA's Blackwell Ultra results, for example, specifically target low-latency, long-context workloads and report substantially better economics than Hopper; AWS is also positioning Blackwell systems directly for reasoning and agentic workloads.

My priority order

I'd probably run a bake-off in this order:

1. Blackwell B200/B300 or GB300 NVL72 — establish your baseline.
2. AMD MI355X — probably the most important alternative to test for long-context workloads. AMD is explicitly pushing its newer Instinct/ROCm stack toward agentic AI.
3. AWS Trainium — especially if you're AWS-native and can commit to the compiler/runtime stack.
4. Google TPU — particularly compelling if your serving architecture can live comfortably in Google's ecosystem.
5. Groq — test separately for latency-sensitive agent loops rather than treating it as a generic GPU replacement.
6. Cerebras — worth testing if your workload is throughput-heavy enough to exploit its unusual architecture.
7. Gaudi 3 — inexpensive experiment, but I'd put less engineering budget behind it initially.

The benchmark I'd actually build

Don't benchmark "tokens/sec." Replay real agent traces:

• 32–128K input contexts
• 1–8K output
• 10–100 sequential tool/model calls per task
• batch sizes 1, 4, 16, 64+
• tool-call pauses and bursty arrival patterns
• prompt-cache/KV-cache reuse
• speculative decoding where available
• your actual quantization and model

Then measure:

$/completed agent task p50/p95 time-to-task-completion $/1M generated tokens tokens/sec/user at your target concurrency KV-cache capacity/utilization prefill vs decode cost power per completed task

That last distinction is important: a chip that wins benchmark throughput can lose badly on an agent workflow if its decode latency, memory behavior, or scheduling causes each sequential step to take longer.

Also, don't assume cheaper silicon automatically means cheaper inference. A recent cross-accelerator study found that optimal hardware changes with batch size, sequence length, and model size, and that software maturity/compilation and idle power can materially change the economics.

If your agents are primarily long-context + batch-1/low-batch + many sequential tool calls, I'd make Blackwell, MI355X, TPU, Trainium, and Groq the core five-way comparison. If they're high-concurrency batch inference, I'd weight Blackwell, AMD, TPU, Trainium, and Cerebras more heavily.

Evaluating chip platforms for long, tool-using AI agent workflows requires looking beyond raw peak FLOPs. Agentic loops are notoriously bottlenecked by memory bandwidth during the Prefill phase (reading long context/history + tool schemas) and interconnect latency during iterative sequential generation (waiting for a tool call, executing it, and feeding it back).

The primary chip platforms and infrastructure paradigms you should evaluate depend on where your cost bottlenecks lie:

• Image: AI inference hardware options spanning specialized ASICs, Wafer-Scale engines, and high-bandwidth GPUs

1. Wafer-Scale Accelerators (e.g., Cerebras WSE)

• The Architecture: Instead of cutting a silicon wafer into individual chips, a wafer-scale engine uses an entire 21-inch silicon wafer as a single massive processor with on-chip SRAM.
• Why it fits agents: Because the entire model (e.g., Llama-3-style 8B or 70B classes) fits directly onto the SRAM of a single wafer-scale system, it completely bypasses the off-chip memory bandwidth bottleneck and cluster interconnect (NVLink/InfiniBand) latency.
• Agent Trade-off:
  • Pros: Blazing-fast token generation and exceptionally low time-to-first-token on supported open-weights models. Cost per million tokens for supported endpoints is heavily competitive with traditional multi-GPU setups (often running ≈ $0.35 to $1.50 per million tokens depending on the tier).
  • Cons: Rigid model constraints—you are limited to the specific model architectures optimized and loaded by the provider.

2. Language Processing Units / Custom ASICs (e.g., Groq LPU)

• The Architecture: Deterministic, SRAM-based architectures designed specifically to eliminate the von Neumann bottleneck inherent in traditional GPUs during sequential token generation.
• Why it fits agents: Tool-using agent loops require fast, iterative back-and-forth reasoning steps. Groq’s LPU architecture excels at raw token velocity (hundreds to thousands of tokens per second), reducing the idle wait time between individual tool calls in a multi-step chain.
• Agent Trade-off:
  • Pros: Ultra-low latency keeps multi-step agent execution moving fast; very aggressive pricing on smaller open-weight models ($0.05 to $0.90 per million tokens).
  • Cons: On-chip SRAM limits massive context windows or giant MoE (Mixture of Experts) architectures compared to high-capacity HBM GPU setups unless heavily memory-pooled. Model catalog is intentionally narrow.

3. Commodity & Enterprise GPUs with High-Bandwidth Memory (NVIDIA H100 Go to product viewer dialog for this item. / B200 Go to product viewer dialog for this item. / AMD MI300X Go to product viewer dialog for this item.)

• The Architecture: The incumbent standard. Platforms like the AMD MI300X Go to product viewer dialog for this item. (with 192GB of HBM3) or NVIDIA H100 Go to product viewer dialog for this item./B200 clusters equipped with massive memory capacity.
• Why it fits agents: Long tool-using workflows mean bloating context windows (system prompts, full conversation history, 20+ tool definitions, JSON schemas). MI300X and high-VRAM NVIDIA setups allow you to host large-context open-weights models (like 70B+ parameters or large MoEs) locally or via serverless GPU providers with massive KV-caching headroom.
• Agent Trade-off:
  • Pros: Ultimate flexibility. You can swap models instantly, fine-tune custom variants, handle massive context lengths, and use advanced serving engines (vLLM, TensorRT-LLM) optimized for continuous batching and PagedAttention.
  • Cons: Higher capital expenditure if self-hosting; standard cloud GPU rental margins mean you must tightly optimize your batching and KV-cache reuse to keep costs down.

4. Specialized Serverless Aggregators & Inference Clouds (The Multi-Cloud Layer)

Instead of locking into a single silicon platform at the hardware level, many agent developers abstract the chip layer entirely by using high-throughput serverless providers (e.g., Fireworks AI, Together AI , or SiliconFlow ) or routing proxies like OpenRouter.

• They run a mix of underlying hardware (NVIDIA H100s, AMD accelerators) tailored per model.
• They handle complex agentic requirements like precise structured outputs , native parallel tool calling , and aggressive KV-caching across multi-turn agent calls.

Recommendation for Evaluation

1. If your agent workflow is heavy on massive context and complex JSON tool schemas: Prioritize high-VRAM GPU infrastructure (like AMD MI300X Go to product viewer dialog for this item. or NVIDIA H100 Go to product viewer dialog for this item. nodes) via efficient serverless providers that support robust prefix/KV-caching , so you aren't re-prefilling the static system prompt and tool definitions on every single tool loop iteration.
2. If your agent relies on rapid, short-burst sequential reasoning loops with standard-sized open models: Test wafer-scale (Cerebras) or LPU (Groq) endpoints to see if the reduction in wall-clock execution time outweighs any architectural constraints.
3. Benchmark the whole chain , not just the micro-benchmark: Measure the cost and latency of a complete 5-step agent task (Reason → Tool Call → Parse → Reason → Final Answer) rather than isolated tokens per second.

If you want to narrow this down, tell me:

• What model size/class (e.g., 8B, 70B, or MoE) are your agents currently relying on?
• Are you looking to buy/rent bare-metal chips or consume this strictly via APIs/serverless clouds?

Building AI agents that execute long, multi-step, tool-using workflows creates a unique economic bottleneck. Unlike standard chat apps, agents continuously cycle through large state histories, repeat planning phases, and trigger heavy structured outputs or tool-calling loops. This means your costs scale across both heavy prefill (reading extensive tool definitions and accumulated logs) and sequential decode (generating intermediate thoughts or API calls).

To optimize your total cost of ownership (TCO) and reduce inference friction, you should evaluate hardware platforms by looking at how they address memory bandwidth, latency, and heterogeneous orchestration.

1. High-VRAM & Bandwidth-Optimized GPUs (The Production Baseline)

• Platforms to evaluate: NVIDIA H200 Go to product viewer dialog for this item. / Blackwell-generation systems Go to product viewer dialog for this item., AMD MI300X Go to product viewer dialog for this item..
• Why they fit long workflows: Long context windows and persistent agent memory devour VRAM capacity and memory bandwidth. Autoregressive decoding requires re-reading the entire model weights from memory for every single token generated.
• The economic angle: While high-end enterprise GPUs carry a steep upfront or hourly price tag, hardware like the MI300X or H200 offers massive high-bandwidth memory (HBM) pools that let you host large open-weight models locally or via dedicated cloud endpoints without aggressive quantization that ruins tool-calling accuracy. Pair these with software optimizations like vLLM or SGLang for advanced KV-caching across agent turns.

2. SRAM-Based Deterministic Accelerators (For Ultra-Low Latency & Fast First Tokens)

• Platforms to evaluate: Groq LPU (Language Processing Unit).
• Why they fit long workflows: Groq eschews traditional HBM for massive on-chip SRAM, bypassing the memory bandwidth bottleneck entirely during generation. It trades massive multi-tenant batch flexibility for radical speed on individual streams—delivering extreme tokens-per-second and ultra-low time-to-first-token (TTFT).
• The economic angle: If your agent workflow stalls out waiting for step-by-step reasoning or if you run tight interactive loops where a user or supervisor agent is waiting on real-time decisions, eliminating tail latency speeds up the entire task lifecycle. It works best when your active models fit cleanly into their static memory compiler constraints.

3. Wafer-Scale Dataflow Engines (For Raw Throughput on Massive Open Models)

• Platforms to evaluate: Cerebras CS-3 / Wafer Scale Engine.
• Why they fit long workflows: By treating an entire silicon wafer as a single giant processor, Cerebras delivers thousands of tokens per second, outstripping standard GPU clusters by orders of magnitude on supported open-weight models (like large Llama configurations).
• The economic angle: While the system footprint is specialized, a single unit replaces racks of conventional hardware for high-throughput inference tasks. If your agent architecture relies on a massive centralized model to stream rapid execution steps or process heavy coding/planning tasks continuously, the raw speed lowers the infrastructure footprint required to service high concurrent agent sessions.

4. Heterogeneous RDU + CPU Blueprints (For Balanced Enterprise Tool Execution)

• Platforms to evaluate: SambaNova DataScale / Reconfigurable Dataflow Units (RDUs) paired with Intel Xeon 6 Go to product viewer dialog for this item. or high-core CPUs.
• Why they fit long workflows: Agent workflows aren't just LLM generation; they spend significant cycles compiling code, querying vector databases, running sandboxed interpreters, and parsing tool outputs. SambaNova's RDU architecture is built to handle large Mixture-of-Experts (MoE) or reasoning models efficiently, while the host CPU infrastructure manages the heavy lifting of agent tool-call execution.
• The economic angle: This approach targets data center drop-in compatibility and reduces the friction of spinning up disconnected auxiliary servers just to run the glue logic and sandboxes attached to your agentic loops.

To help narrow down which platform fits your exact cost and architecture goals, tell me:

• What is the average context length and output token volume per agent task?
• Are you deploying on-premise/air-gapped , or looking at cloud inference APIs/dedicated host providers?
• Which open-weight or proprietary models are you standardizing on for your tool-calling loops?

For long, tool-using agent workflows, I’d evaluate inference platforms based on cost per successful task, not just $/M tokens. Tool loops amplify latency, and long contexts make memory bandwidth/prefix handling especially important.

Platforms I’d put in the bake-off

For your workload, I'd prioritize three

1. Cerebras — sequential agent loops

This is the one I'd test first if an agent typically does:

prompt → model → tool → model → tool → model → final answer

The important metric isn't merely tokens/sec. If each model call is on the critical path, high generation speed can reduce wall-clock agent latency substantially. Cerebras explicitly targets coding, automation and agentic workloads, and its current inference service supports function calling and structured outputs.

There's also a practical integration advantage: its API is OpenAI-compatible, so it can be relatively inexpensive to A/B test.

2. Groq — cheap fast "worker" models

I'd test Groq for the numerous lower-complexity calls surrounding your main reasoning model: classification, extraction, routing, tool selection, summarization, etc.

Its current pricing makes small models particularly attractive: Llama 3.1 8B is listed at $0.05/M input and $0.08/M output, while GPT-OSS 20B is $0.075/$0.30.

Groq also offers batch processing at 50% lower cost, which is interesting for asynchronous agent workloads.

3. Blackwell — your control/baseline

Don't skip NVIDIA even if you're trying to get away from it. It gives you the most mature ecosystem and lets you determine whether the alternatives actually win once you account for:

• KV-cache memory
• long-context throughput
• batching
• quantization
• tool-call latency
• engineering/ops overhead
• model availability

The key thing I'd benchmark

For agent workloads, I'd build a benchmark around cost per completed task:

cost/task =
  input tokens
+ cached input tokens
+ output tokens
+ tool-call overhead
+ retries
+ failed trajectories

and measure:

1. $ / successful task
2. p50/p95 time-to-final-answer
3. tokens/sec while generating
4. prefill latency for 32K/128K+ contexts
5. tool-call round-trip latency
6. KV-cache efficiency
7. success rate / tool-call accuracy

That can produce surprising results. A chip that's 2× cheaper per token can still lose if it causes agents to spend 30% longer waiting between sequential calls—or if it requires enough engineering that the infrastructure savings disappear.

One particularly interesting architecture

I'd seriously consider a heterogeneous agent stack rather than picking one accelerator:

Fast/cheap accelerator → routing, extraction, tool selection, simple calls

Cerebras/Groq → high-frequency reasoning/tool-loop calls

Blackwell/TPU → large-context or heavyweight reasoning

CPU → tool execution / orchestration

That maps hardware to the actual economics of an agent rather than treating every inference call identically.

And I would explicitly benchmark long-context prefill + repeated-prefix caching. In tool-using agents, the same system prompt, tool definitions, conversation history and accumulated state can get resent dozens of times. The economics of that repeated context can dominate output-token pricing.

One current data point that makes Cerebras/Groq particularly worth testing: Cerebras' published service now exposes high-throughput inference with explicit agentic positioning, while Groq's current prices are already very aggressive for open models.

If you tell me roughly model size (e.g. 30B/120B/400B), context length, tokens/sec, and calls per agent trajectory, I can turn this into a concrete 2026 chip/platform shortlist with estimated $/1,000 agent runs and a benchmark plan.

Yes. For long, tool-using agent workflows, I’d evaluate hardware differently from conventional LLM serving. The key metric is usually $/completed agent task at a target end-to-end latency, not raw tokens/sec. Tool calls create idle gaps, short decode bursts, repeated KV-cache activity, and often relatively low average accelerator utilization.

My shortlist

The two I'd put at the top of the POC

1. AMD MI355X

This is the safest "let's see if we can materially lower inference cost without abandoning conventional GPU serving" bet. AMD's recent work is unusually relevant to your use case: distributed MoE inference, KV-cache movement, speculative/multi-token prediction, SGLang, and low-concurrency optimization.

AMD reports a particularly interesting production-like comparison: at 129 tok/s/user, its MI355X + SGLang configuration achieved $0.173/M tokens versus $0.178/M for B200 + Dynamo/TRT-LLM in its benchmark. That's a vendor-reported result, so I'd reproduce it independently, but it indicates the gap isn't purely theoretical.

2. SambaNova SN50

I'd put this on the POC list specifically because your workload is agentic, rather than simply "serve a chatbot cheaply." SambaNova's architecture attacks data movement and claims 5× maximum speed and >3× throughput versus B200 on its agentic benchmarks, with claims of substantially lower TCO.

That's exactly the kind of architecture that can win when the workload is:

reason → tool → reason → tool → reason → final

rather than a giant batch of independent requests.

Don't benchmark only tokens/sec

I'd build the evaluation around something like:

cost / successful agent trajectory

and measure:

• TTFT
• inter-token latency
• tokens/sec at concurrency 1, 4, 16, 64
• input/output token ratio
• KV-cache memory consumption
• prefill vs decode utilization
• time spent waiting on tool calls
• recovery/retry behavior
• tokens/$
• completed agent tasks/$
• energy/task
• model-switching overhead
• quantization quality
• framework optimization effort

The concurrency dimension is particularly important. A chip that is spectacular at 64-way batching can be a terrible choice for agents that spend most of their time doing:

1 request → 800 tokens → tool call → 1 request → 500 tokens → tool call → ...

AMD's own published results illustrate this: its optimization work specifically distinguishes high-throughput/high-concurrency kernels from low-concurrency kernels.

One more important consideration

For very long workflows, I'd seriously consider a heterogeneous architecture rather than picking one accelerator:

cheap accelerator for routine agent turns + premium accelerator for difficult reasoning turns

For example:

router → MI355X / TPU / SN50 → tool execution → cheap decode → escalation to B200/GB300 when needed

That can produce much larger savings than optimizing the average token price, because agent workloads often have a highly skewed distribution of reasoning difficulty.

Also benchmark prefill and decode separately. For long-context agents, you may find that your real bottleneck is KV-cache movement rather than matrix compute. Ironwood, MI355X, Groq and SambaNova are particularly interesting precisely because their architectures attack that memory/data-movement problem in different ways.

My practical evaluation order:
B200/GB300 baseline → MI355X → SN50 → Ironwood → Trainium3 → Groq/Cerebras.

If you're primarily cloud/API rather than owning hardware, the ordering changes: I'd benchmark providers serving these chips, because utilization, batching, pricing, and scheduler quality can dominate the underlying silicon.

For long, multi-step, tool-using AI agent workflows, your primary cost and latency bottlenecks are memory bandwidth during token generation (decode phase) and KV cache bloat as the agent accumulates conversational history, file states, and tool outputs over dozens of loops. To minimize total cost of ownership (TCO), you should evaluate a mix of state-of-the-art rack-scale GPU systems, reconfigurable dataflow architectures, and high-bandwidth alternative accelerators.

Top Chip Platforms to Evaluate

• • NVIDIA Blackwell Ultra (GB300 NVL72): The current enterprise gold standard for rack-scale inference, delivering massive TCO reductions (up to 35x lower cost-per-token than Hopper) for low-latency agentic loops via a unified NVLink fabric and native FP8/FP4 support. Use TensorRT-LLM to optimize execution.
• • AMD Instinct (MI325X / MI300X): Offers high-capacity VRAM and strong memory bandwidth well-suited for serving large open-weight models (like the Qwen or Llama families) with robust ROCm backend support inside engines like vLLM.
• • SambaNova (RDU - Reconfigurable Dataflow Unit): Replaces traditional kernel-by-kernel GPU execution with a spatial dataflow graph that keeps computation and communication synchronized, reducing the multi-step communication overhead common in agent loops [0.25].
• • Groq (LPU - Language Processing Unit): Extreme low-latency deterministic inference that excels if your agent workflows are bottlenecked by Time-to-First-Token (TTFT) and rapid, short sequential handoffs during tool invocation.
• • Google Tensor Processing Unit (TPU v5p / Ironwood): Highly optimized for massive scale and cost-effective serving of transformer-based architectures, especially if deployed within Google Cloud's native inference stacks.

Key Architectural Metrics to Benchmark

If you share your target model sizes (e.g., 8B/70B open-weights vs. frontier closed APIs) and average context length per workflow , I can help narrow down whether a cloud provider or on-premise cluster fits your budget best.

For long, tool-using agentic workflows—which involve massive context lengths, iterative reasoning loops, JSON tool-call parsing, and bursty execution—optimizing for raw floating-point operations (FLOPs) is a trap . Agentic workflows are severely memory-bandwidth bound (during autoregressive token generation) and latency-sensitive during the prefill phase when swallowing large conversation histories and tool definitions.

Evaluating chip platforms requires balancing cost, high-bandwidth memory (HBM) capacity for large KV caches, and software-stack maturity.

1. High-Bandwidth & High-VRAM GPUs (The Production Workhorses)

Because long tool-using workflows accumulate huge KV caches per active agent thread, you need accelerators with massive VRAM and high memory bandwidth to prevent thrashing.

Best cost-to-VRAM alternative to NVIDIA. Each MI300X features 192 GB of HBM3 memory and massive bandwidth . This allows you to host larger open-source models (like Llama-3/DeepSeek variations) with fewer sharded GPUs, significantly lowering the parallelism overhead and cost per token compared to older Hopper setups. Software maturity via ROCm has caught up significantly for mainstream inference frameworks.

The gold standard for ultra-long context. While upfront and hourly costs are higher, the H200 ’s 141 GB of HBM3e and Blackwell's Go to product viewer dialog for this item. massive memory fabrics drastically reduce Time-to-First-Token (TTFT) on massive system prompts and multi-megabyte tool schemas . If using managed cloud, look for specialized providers running bare-metal H200 pods rather than traditional hyperscalers to cut compute overhead by 30–45%.

Best mid-tier budget option for moderate traffic. Packing 48 GB of GDDR6 VRAM, it's a cost-efficient choice if you run quantized open-source models (8-bit or 4-bit) for specialized, narrower agent sub-tasks rather than massive frontier models.

2. Custom ASICs & Spatial Architectures (The Efficiency Play)

Agent loops often leave traditional GPUs underutilized (30–40%) due to the alternating cadence of CPU orchestration, I/O bound tool execution, and token bursts. Specialized silicon handles this differently:

• Google Cloud TPUs (v5e / v6e)Go to product viewer dialog for this item.: Highly cost-efficient for scaled serving via XLA paths. Google’s architecture handles large, static KV caches effectively and delivers exceptional performance-per-dollar for predictable high-volume serving compared to standard GPU clusters.
• Cerebras Wafer-Scale Engine (CS-3): An exceptional architectural choice for disaggregated inference . Because agent workflows feature massive prompt prefill (feeding tool specs and history), pairing a wafer-scale engine to handle instant, massive prefill alongside a secondary memory-optimized card for token decode offers massive speed and structural cost advantages.
• Groq LPUs (Language Processing Units): Exceptional for deterministic, low-latency execution in environments where agent response time and rapid tool-loop turnaround dictate success, though constrained by on-chip memory limits relative to multi-hundred-gigabyte HBM GPUs.

Key Architectural Strategies to Pair with Your Silicon

Choosing the chip is only half the battle; agent economics change drastically based on how you deploy software and handle state:

1. Prompt Caching: Ensure your inference provider or self-hosted stack natively supports aggressive prompt caching . In tool-using agents, the system prompt and tool definitions rarely change between turns—caching them drops input token costs significantly.
2. Disaggregated Prefill/Decode: Separate the phase that ingests the long tool context (prefill) from the phase that streams the output tokens (decode), routing them to hardware optimized for bandwidth vs compute.
3. Hardware-Agnostic Runtimes: Use serving runtimes like vLLM or Modular MAX to optimize KV cache management (PagedAttention) and maximize hardware utilization across heterogeneous environments.

To help narrow down the ideal platform, tell me:

• What is your estimated concurrent agent volume and daily token consumption?
• Are you aiming to self-host on bare metal or consume via managed API/GPU cloud?
• Are you tied to a specific open-source model family (e.g., Llama 3, DeepSeek)?

For long, tool-using agent workflows, I would evaluate hardware differently from ordinary chat inference. Your bottlenecks are often decode latency, KV-cache capacity/bandwidth, long-context prefill, and utilization under bursty workloads, not just peak FLOPs. Recent accelerator research also finds that the best platform changes materially with sequence length, batch size, and model size.

My shortlist

1. Don't automatically eliminate NVIDIA

I'd benchmark Blackwell B200/B300 and GB300 NVL72 as your reference implementation even if your goal is to get away from NVIDIA.

NVIDIA's current inference stack is unusually well optimized around exactly the things agents stress: FP4, speculative decoding, MoE parallelism, KV/cache management and prefill/decode disaggregation. NVIDIA reports GB300 NVL72 at $0.123/million tokens in one April 2026 InferenceX configuration, though that's a vendor-reported benchmark rather than a universally applicable price.

The key question isn't "Can another chip do more tokens/$ in a synthetic benchmark?" It's:

Can it maintain that advantage after our actual agent orchestration, context lengths, batching, tool-call pauses and model-specific kernels are included?

2. AMD MI355X is probably the first alternative I'd test

For a company specifically chasing cheaper inference, I'd put MI355X near the top of the bake-off.

AMD's MI350 generation is explicitly targeted at high-speed inference, and the hardware's large HBM footprint is particularly relevant to long-context workloads.

The interesting part is that the economics can look substantially better than Blackwell once you get the ROCm stack properly tuned. But don't judge it using an out-of-the-box deployment: kernel fusion, quantization, attention/MoE implementations and serving framework choices can completely change the result. Real-world evaluations have found AMD competitive in some configurations while NVIDIA pulls away when the newest inference optimizations are simultaneously enabled.

I'd make MI355X your #1 "can we cut infrastructure cost without radically changing our architecture?" experiment.

3. AWS Trainium3 deserves serious attention

If you're already heavily invested in AWS, Trainium can be particularly attractive because you aren't just buying an accelerator—you get AWS's inference infrastructure and Neuron software stack around it.

AWS positions Inferentia specifically for low-cost inference, while newer Trainium generations are increasingly being used for inference as well. Inferentia2, for example, supports distributed inference across chips and is designed for large models.

There's also a particularly interesting development for your workload: AWS + Cerebras are pursuing disaggregated inference where Trainium handles prefill and Cerebras handles decode.

That's almost tailor-made for agent workloads because you can optimize the two very different phases independently.

4. Cerebras is unusually interesting for agents

I'd test Cerebras CS-3 if your agents spend a lot of time generating sequential output.

Agentic workloads can generate dramatically more tokens than ordinary chat; Cerebras cites roughly 15× more tokens per coding-agent query in one AWS announcement.

Cerebras attacks the decode bottleneck differently from GPUs: its wafer-scale architecture puts the model in extremely high-bandwidth on-chip SRAM rather than repeatedly pulling weights from external HBM.

That's potentially very attractive for:

• coding agents
• reasoning agents
• tool-selection loops
• long chains of short generations
• workloads where time-to-next-token matters more than aggregate throughput

And the AWS integration makes it much more practical to evaluate than it used to be.

5. TPU/Ironwood is the "large-scale efficiency" bet

I'd include Google's latest TPU generation if you're willing to invest in a less portable stack.

Google's TPU evolution has focused heavily on HBM capacity/bandwidth, scale and power efficiency; the latest research describing Ironwood reports substantial gains across those dimensions.

This becomes more attractive when you have large, predictable inference volume and can amortize the engineering required to optimize the TPU stack.

What I'd actually build for the benchmark

Don't benchmark tokens/sec alone. Build a replay system from your real agent traces and measure:

1. $/completed agent task
2. $/output token
3. P50/P95 time-to-first-token
4. P50/P95 time-to-next-token
5. P95 end-to-end task latency
6. KV-cache memory utilization
7. prefill vs decode cost separately
8. performance at 1, 4, 16, 64, 256 concurrent agents
9. performance at 32k / 128k / 256k+ context
10. quality/regression under quantization

Most importantly, replay the entire workflow, including the periods where the model is waiting for tools. A chip that is 2× cheaper per token but sits idle while your agent waits for a browser/API/database call may not actually reduce your infrastructure bill.

The architecture I'd investigate

For your particular workload, I'd be especially interested in disaggregated prefill/decode:

Long context → prefill accelerator → KV/cache → fast decode accelerator → tool call → repeat

Rather than insisting that one accelerator do everything.

AWS/Cerebras are already pursuing essentially this idea with Trainium + CS-3.

So my initial 2026 evaluation matrix would be:

Blackwell B300/GB300 → AMD MI355X → Trainium3 → Cerebras CS-3 → TPU/Ironwood → Groq

And I'd keep NVIDIA as the control, rather than assuming the cheaper silicon wins. For agentic inference, software stack + memory behavior + utilization can overwhelm the nominal chip-price difference.