ilkerandClaude Opus 5
dc38dd6096
Inserted between quota checks and scheduling: admission answers when a request may proceed, the scheduler still answers where it goes. Keeping them apart is the point — an engine queue is FIFO and knows nothing about tenants, so once a batch job is in it, no endpoint selection rescues the request behind it. - saturation detector: one number per pool, the worse of queue depth and KV occupancy, since either alone misleads. - priority bands with per-band saturation ceilings, so the cluster degrades from the bottom up. - tenant round-robin within a band: one caller sending a thousand requests takes one slot in the rotation, not a thousand. - bounded queues with capacity (503) and TTL (504); cancelled requests leave the queue instead of holding capacity. - metrics for queue depth, wait time, rejections and pool saturation. Exit criterion, measured in test/e2e: with 500 concurrent bronze requests running continuously, interactive p99 in the platinum band stays at 1.04-1.24x idle against a 1.5x budget. With admission off the same load pushes it to 55-60x. Both directions are asserted, so the test fails if admission regresses and also if the load generator stops being a real load. Two lags had to be closed, and they needed different answers. Engine metrics are scraped and lag an interval; router inflight does not, but it counts running requests, and treating those as queue makes a healthy engine look overloaded every interval. What is actually queued is inflight minus batch capacity, and capacity is not in the metric contract, so it is learned as the high-water mark of observed running. Separately, between admission granting a request and the router dispatching it the request is invisible everywhere, so a simultaneous burst passed the check en masse; the controller now counts its own outstanding leases against dispatched inflight. One plan correction: a saturation ceiling of 1.0 was documented as "never held back". It is not — it means "flow up to the healthy limit". A band that must never wait needs a ceiling above 1. Plan §7.2.2 updated. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
llmgw
A single-binary LLM gateway for clusters with multiple InfiniBand fabrics and heterogeneous GPUs.
It serves LLMs running on your own GPU servers (vLLM / SGLang) behind one OpenAI-compatible endpoint. Design document: llmgw-plan.md.
Status
Releases track the roadmap phases; see CHANGELOG.md.
| Version | Phase | Scope | State |
|---|---|---|---|
| v0.1.0 | 0 | Skeleton and sim-engine | released |
| v0.2.0 | 1 | Static router (filter/score/pick, streaming proxy) | released |
| v0.3.0 | 1.5 | Admission control (priority bands) | released |
| v0.4.0 | 2 | Agent: GPU/IB discovery, single-node engines | planned |
| v0.5.0 | 3 | Control plane: catalog, scheduler, reconciler | planned |
| v0.6.0 | 4 | Multi-node placement and fabric awareness | planned |
| v1.0.0 | 5 | Tenancy, prefix affinity, benchmarking | planned |
As of v0.3.0 the router serves traffic and protects it: a batch workload no longer starves interactive requests. Still missing before v1.0.0: authentication, rate limits and budgets, and the control plane that replaces the static endpoint list.
Building
make build # bin/llmgw
make test # unit tests
make race # with the race detector
Running the router
# Two fake engines and a router in front of them.
./bin/llmgw sim-engine --listen :8001 --model llama &
./bin/llmgw sim-engine --listen :8002 --model llama &
./bin/llmgw server \
--endpoint e1=http://127.0.0.1:8001=llama \
--endpoint e2=http://127.0.0.1:8002=llama
curl -N localhost:8080/v1/chat/completions \
-H 'content-type: application/json' \
-H 'x-session-id: demo' \
-d '{"model":"llama","messages":[{"role":"user","content":"hello"}],"stream":true}'
For anything real use a config file; deploy/examples/server.yaml
is commented throughout. Metrics, health and pprof are on the admin port (:9090
by default), deliberately separate from the client-facing one.
Requests carry a priority band (x-llmgw-band). Under load, low bands wait at
the gateway while high bands keep flowing, so the cluster degrades from the
bottom up instead of everywhere at once. Measured effect: interactive p99 stays
within 1.24x of idle while 500 concurrent batch requests run, against 58x with
admission off.
Routing profiles select how endpoints are scored: chat favours session
affinity, batch favours the shortest queue, long-context favours free KV
cache. Pick one per model in the config, or per request with the
x-llmgw-profile header.
sim-engine
Simulates the inference-engine behaviours the router cares about, so the router can be developed and tested without GPUs. What it models:
- continuous batching (bounded number of concurrent sequences)
- KV cache block accounting, queueing once blocks run out (head-of-line blocking included)
- automatic prefix caching (chained block hashes plus an LRU) and its effect on time to first token
- per-token latency growing with batch size
- dropping a request and freeing its blocks when the client disconnects
/metricsusing vLLM's metric names
./bin/llmgw sim-engine --listen :8000 --model llama-3.3-70b
curl -N localhost:8000/v1/chat/completions \
-H 'content-type: application/json' \
-d '{"messages":[{"role":"user","content":"hello"}],"max_tokens":8,"stream":true}'
Tune load and latency behaviour with --max-num-seqs, --num-gpu-blocks,
--prefill-tps, --decode-tps, --batch-contention and
--prefix-cache-blocks. For chaos testing use --fail-rate, and --seed for
deterministic runs.
The engine metric contract
For the router to make routing decisions an engine must expose these three metrics (plan §7.6). sim-engine publishes them under vLLM's real names:
vllm:num_requests_running
vllm:num_requests_waiting
vllm:kv_cache_usage_perc
Adding support for a new engine means satisfying that contract. Metric names drift between versions, so the name mapping is configurable.
Module path
gitea.mynodes.xyz/ilker/llmgw. To change it:
make rename NEW=git.example.com/team/llmgw
Language
Code, comments, tests, commit messages and documentation are in English.