An Inference Storage Solution for Enterprise Code-Assistant Platforms
Code assistants are the archetypal long-context, high-frequency-recovery workload: codebase contexts run to tens of thousands of tokens and developers close and reopen at will. External KV measured: 32B single-GPU recovery TTFT 37.97 s → 9.30 s; 480B 8.6–20× vs re-compute.
The code-assistant workload profile
Context composition: repository structure + relevant files + session history — easily past ten thousand tokens; and code models themselves keep growing (the measured load, Qwen3-Coder-480B, is precisely a code model). Developer behavior is stop-and-go: close the IDE, switch branches, continue tomorrow — each time a cold recovery.
The no-external-tier outcome is measured: the 480B re-compute baseline posts TTFT p50 149.5 s (R2) — developers waiting two and a half minutes for the first token means the tool simply goes unused.
Measured data by tier
Mid-size model (Qwen2.5-32B, single GPU): cold-read recovery TTFT 37.97 s → 9.30 s (4.1×, R1, after the parallel-read patch). Flagship code model (Qwen3-Coder-480B-FP8, TP=8): read-back 11.85 s vs 149.5 s recompute (12.6×), throughput 74.9 vs 4.1 tok/s (R2).
An engineering detail: repository-level shared prefixes (team members on the same codebase) deduplicate naturally in the external pool (R3 cross-instance sharing) — the bigger the team and the hotter the repo, the higher the hit rate. A positive externality unique to the code-assistant scenario.
FAQ
Code changes constantly — is the cache still useful?
Yes: invalidation is block-granular (blocks after the changed file recompute, blocks before still hit), and session history plus repository structure change slowly. The measured load simulates exactly this recovery pattern (R2).
What about private-code security?
KV blocks stay inside the private cluster, tenant-isolated, never leaving the enterprise boundary — a cleaner data boundary than calling public-cloud code-assistant APIs.
At what team size is self-hosting worth it?
It depends on concurrency and compliance requirements; the measured band covers concurrency 8–32 (R2). Run a G1–G4 joint test with the team's real usage traces — do not build if the gates fail.
Data sources (verifiable)
Related reading
- Session-Recovery Acceleration for Agent and Code-Assistant Platforms
- Long-Context Cold Recovery: The Main Performance Battleground of the Agent Era
- The Storage Challenge of MoE Inference: A 480B Measured Case
Joint test first, decisions second: gate-based acceptance with built-in stop-loss
The full costing model is provided as reproducible Python after NDA — customers can rerun it with their own parameters. Every key figure on this site carries a report ID and is open to third-party verification.
This site presents business-cooperation information and constitutes neither an investment offer nor any promise of returns. Measured data come from signed / official test reports (see the Evidence Library); vendor specs, public sources and estimates are labeled as such.