Ilker ManapandClaude Opus 5
5b425c32e5
A release was a git tag. Installing llmgw on a node meant having a Go toolchain, and the onboarding script copied a binary from wherever it was run — which works and quietly makes whoever runs it the build server, with the fleet ending up on whatever was in their working tree. Tags now build static linux/amd64, linux/arm64 and darwin binaries and attach them. CGO_ENABLED=0 is the point: it has to run on a node whose libc is older than the builder's, and llmgw has no C dependency because modernc.org/sqlite is pure Go precisely so this stays true. Verified: "not a dynamic executable", runs on a node, reports its version. `ctl engine-check` closes the other half of the phase 4 gap. llmgw's launch arguments are read from each engine's documentation, and engines rename flags between versions; when that happens the failure arrives minutes into a model load, in the engine's own error message, on a node nobody was watching. It builds the widest command llmgw would ever produce — multi-node, since a flag that only appears there is the one nobody notices has gone — and checks each flag against the engine's own --help. No GPU: a CLI prints its help anywhere. Verified against llama.cpp b10991, all seven flags accepted. Flags are matched on a word boundary, so --port is not satisfied by --port-range. Without that, a renamed flag would sail through the check that exists to catch renamed flags. It also warns when the help text looks truncated, which is how the first run went: llama.cpp's help cut to 400 of its 725 lines reported five flags as missing that the running cluster uses. TestSidecarRunsBeforeEngine was flaky under a full run, and worse than flaky — its wait loop fell through on timeout, so a slow machine turned the ordering assertion into a no-op and the failure surfaced later as something else. It now asserts, with a deadline that suits a machine running every other package at the same time. 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, engine supervision | released |
| v0.5.0 | 3 | Control plane: catalog, placement, reconciler, ctl | released |
| 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
Operating a cluster
llmgw ctl nodes # capacity, health, what is running where
llmgw ctl models # catalog and replica counts
llmgw ctl replicas --model llama # replicas and their shards
llmgw ctl explain-placement llama # why a model has not been placed
llmgw ctl drain gpu-a-01 # move work off a node, one replica at a time
llmgw ctl cordon gpu-a-01 # stop new placement, leave what runs
ctl talks to the admin port (:9090 by default), not the client-facing one.
Add --json to any command for machine-readable output.
explain-placement is the one worth knowing about: "unschedulable" on its own
says nothing about which of six constraints bit, so it prints every candidate
considered and the reason it was rejected.
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.