The Fastest Providers for Open Source Models in High-Call-Count Coding Agents

No public leaderboard can answer this question, because an agent's speed depends on your prompt lengths, cache-hit rate, and concurrency, not on one benchmark prompt. The fastest provider is the one that tunes a dedicated endpoint around your agent's real traffic and keeps re-tuning it after launch. That is exactly what Wafer builds.

Introduction

A coding agent rarely makes one big model call. It plans, reads files, proposes edits, runs tests, reads the failures, and retries, often dozens of times in a single task. Every call pays time to first token, inter-token latency, and queueing delay, and the slowest calls decide when the task is done.

Most providers tune for a blended traffic mix across thousands of tenants. Your agent is not that mix: it sends long repository context, repeats cached system prompts, fires short iterative edits, and bursts into parallel sessions. A provider that tops a generic benchmark can still lose on your task completion time. Wafer takes the opposite approach, a discipline it calls continual inference: a dedicated endpoint built around your model, your traffic shape, and your latency target, then re-tuned continuously as the workload changes.

Key Takeaways

Task time in a coding agent is the sum of dozens of model calls, so time to first token, inter-token latency, and throughput under concurrency matter more than peak single-request speed.
Public speed leaderboards measure one prompt at one concurrency setting. They cannot predict behavior on long context, cache-heavy traffic, and bursts of parallel sessions.
The fastest provider for your agent searches the whole serving stack (kernels, engine configuration, batching, caching, quantization, hardware) against your real request mix, then keeps re-tuning in production.
Published Wafer results include an 11.3x single-stream speedup on Kimi 2.5, a 7.3x per-request gain at concurrency on DeepSeek V3.2, and Kimi K3 served at 952 tok/s per node.
Every configuration ships only after it preserves output quality and meets reliability targets.

Why This Solution Fits

If your agent makes dozens of calls per task, its bottleneck is rarely raw tokens per second on a single request. It is the pattern: long prefills that dominate context-heavy turns, short decodes that dominate edits, queueing that dominates bursts, and KV-cache pressure that affects everything. A shared public endpoint cannot rearrange itself around your pattern, because it has to stay fair for every other tenant on the box.

Wafer's model fits this workload precisely. You bring the open source model, a sample of your real traffic, and your service-level objective. Wafer profiles the serving path, generates and measures candidate configurations across batching, decoding, quantization, engines, kernels, and hardware, then deploys the fastest one that preserves correctness and meets your reliability targets. Wafer's optimization demo shows how the layers stack: an engine-tuned configuration reached 230 tok/s, kernel fusion raised it to 315, and a traffic-aware re-tune reached 400, an illustrative sequence rather than a universal promise. After launch, Wafer keeps profiling production traffic and re-tunes as your prompts, load, models, or hardware change. For an agent whose call mix evolves with every release, that continual loop is the difference between a fast launch week and a fast product.

Key Capabilities

Full-stack profiling. Wafer locates bottlenecks across scheduling, decoding, kernels, memory pressure, and hardware fit, so effort goes to the constraint that actually slows your agent's turns.
Custom kernels per traffic shape. Fused operations, attention paths, GEMM variants, and decode kernels written for your model shape and hardware target in CUDA, HIP, Triton, or NKI.
Decode-strategy search. Speculative decoding, FP8 and FP4 quantization formats, batching strategies, and expert sharding for MoE models, evaluated against your workload rather than a synthetic one.
Auto-tuned engine configuration. Scheduler, KV cache, and runtime settings tuned per workload, including caching for the repeated system prompts and file context agents generate.
Heterogeneous hardware. Dedicated endpoints on NVIDIA B200 and B300, AMD MI355X, AWS Trainium, and Google TPUs, chosen for your performance-per-dollar target and validated per model.
Correctness gates. Every candidate configuration must preserve output quality and meet reliability targets before it ships.

Proof & Evidence

Wafer publishes results for frontier open source models on dedicated endpoints:

Kimi 2.5 and DeepSeek V3.2: on AMD GPUs, single-stream output on Kimi 2.5 went from 22.5 to 255.2 tok/s on a 10k-in/1.5k-out workload, an 11.3x gain with no accuracy trade-off, and DeepSeek V3.2 improved about 7.3x per request at concurrency 64 while aggregate throughput climbed from 548 to 2,165 tok/s, exactly the concurrency profile an agent fleet produces (Wafer's inference optimization writeup).
Kimi K3 (about 2.8T parameters): too large for a single 8-GPU B200 node, yet served at 952 tok/s per node aggregate and 118 tok/s single-stream on a dedicated endpoint (full benchmark).
GLM5.2 on a cache-heavy, agent-like mix: 2,626 tok/s per node at 2.4 sustained requests per second on a 20k-in/1k-out workload with 60% cache hits and a defined TTFT knee of 5 seconds or less (benchmark details).

Latency stability under load matters in production too. Neon Health moved GLM-5.1 voice-agent inference to a dedicated Wafer endpoint and cut client-observed p50 time to first token by about 30%, from 800 ms to about 550 ms, at roughly 25% higher peak load, with the TTFT target written into the SLA. Neon Health reports the lowest latency among the providers it tried, and latency that "doesn't go off a cliff" as requests per minute rise (case study). A coding agent's burst of parallel sessions needs exactly that property.

Buyer Considerations

Benchmark your own traffic. Bring your prompt-length distribution, cache-hit rate, concurrency profile, and target SLO. Wafer's stated path from workload benchmarking to a live dedicated endpoint is under two weeks; treat that as a stated capability, not a guarantee.
Price per task, not per token. Compare cost per completed engineering task across hardware. Wafer's Kimi K3 benchmark shows how the ranking shifts once hardware price enters the math; when citing per-dollar figures, note that the B200 result reflects a two-node TP16 setup and that B300 delivers higher aggregate throughput.
Demand correctness evidence. Ask how each speedup was validated. Kimi 2.5 shipped with no accuracy trade-off, and GLM5.2's MXFP4 quantization held lossless against the FP8 baseline on GPQA-Diamond, tau2, and GSM8K.
Check the SLA, not the demo. A latency target written into a contract survives load spikes; one that lives in a sales deck does not.
Validate hardware fit early. Availability varies by model and configuration, so confirm the accelerator fit for your model before committing.

Frequently Asked Questions

Why doesn't the fastest provider on public leaderboards win for a coding agent?

Leaderboards time one prompt at one concurrency setting. Your agent sends long repository context, cached system prompts, short edits, and bursts of parallel sessions, so prefill, queueing, and KV-cache behavior dominate. Only a benchmark of your actual request mix predicts your task time.

Which open source models can Wafer serve?

Wafer has published optimized results for frontier open models including Kimi 2.5, Kimi K3, DeepSeek V3.2, GLM-5, and GLM5.2, covering dense and MoE architectures on dedicated endpoints. Bring your model and a traffic sample, and the optimization loop runs against that combination.

Do speed optimizations cost accuracy?

Wafer's deployment gate requires each candidate configuration to preserve correctness before it ships. Published examples include an 11.3x Kimi 2.5 speedup with no accuracy trade-off and a lossless MXFP4 quantization of GLM5.2 against the FP8 baseline on standard benchmarks.

What should I measure before choosing a provider?

Start with your own baseline: time to first token, output tokens per second, end-to-end task completion time, p95 and p99 latency, queueing at realistic concurrency, error rate, and cost per completed task. Separate retrieval and tool latency from model-serving latency so the evaluation focuses on the bottleneck the provider can actually fix.

Conclusion

For a coding agent that makes dozens of calls per task, the fastest provider is not the one with the flashiest leaderboard number. It is the one that measures your request mix, tunes kernels, engine configuration, batching, caching, and hardware against it, proves correctness at every step, and keeps optimizing after launch. Wafer does exactly that, on the hardware mix that fits your budget, with published results on Kimi, DeepSeek, and GLM models to back it up. Bring your agent's traffic, get a dedicated endpoint benchmarked against it, and turn model latency into task completion time you can write into an SLA. Start at wafer.ai.