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Evaluate vllm-mlx as high-performance inference backend #1069

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opened 2026-03-23 12:53:17 +00:00 by perplexity · 1 comment
perplexity commented 2026-03-23 12:53:17 +00:00
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Parent: #1063

Objective

Test vllm-mlx as an alternative inference backend to Ollama for speed-critical scenarios. The PDF notes MLX is 25–50% faster than Ollama with 15% less memory usage, and vllm-mlx offers MLX performance with an OpenAI-compatible serving API.

Priority

P2 — Medium. Ollama is the recommended starting point for ecosystem compatibility. Evaluate vllm-mlx once the primary Ollama stack is stable.

Context

  • EuroMLSys '26 paper confirmed 21–87% higher throughput than llama.cpp across configurations
  • LM Studio vs Ollama comparison on M3 Ultra: MLX achieves +38% to +59% speed advantage
  • vllm-mlx provides OpenAI-compatible API, so existing tool-calling code should work

Steps

  1. Install vllm-mlx on M3 Max
  2. Load Qwen3-14B via MLX backend
  3. Benchmark: compare tok/s, time-to-first-token, and memory usage against Ollama
  4. Test tool-calling API compatibility (OpenAI-compatible endpoint)
  5. Document results and recommendation (switch vs stay on Ollama)

Acceptance Criteria

  • Side-by-side benchmark data: Ollama vs vllm-mlx for Qwen3-14B
  • Tool calling works identically through both backends
  • Clear recommendation with tradeoffs documented
Parent: #1063 ## Objective Test vllm-mlx as an alternative inference backend to Ollama for speed-critical scenarios. The PDF notes MLX is 25–50% faster than Ollama with 15% less memory usage, and vllm-mlx offers MLX performance with an OpenAI-compatible serving API. ## Priority P2 — Medium. Ollama is the recommended starting point for ecosystem compatibility. Evaluate vllm-mlx once the primary Ollama stack is stable. ## Context - EuroMLSys '26 paper confirmed 21–87% higher throughput than llama.cpp across configurations - LM Studio vs Ollama comparison on M3 Ultra: MLX achieves +38% to +59% speed advantage - vllm-mlx provides OpenAI-compatible API, so existing tool-calling code should work ## Steps 1. Install vllm-mlx on M3 Max 2. Load Qwen3-14B via MLX backend 3. Benchmark: compare tok/s, time-to-first-token, and memory usage against Ollama 4. Test tool-calling API compatibility (OpenAI-compatible endpoint) 5. Document results and recommendation (switch vs stay on Ollama) ## Acceptance Criteria - Side-by-side benchmark data: Ollama vs vllm-mlx for Qwen3-14B - Tool calling works identically through both backends - Clear recommendation with tradeoffs documented
claude self-assigned this 2026-03-23 13:44:25 +00:00
claude added the harnessinferencep0-critical labels 2026-03-23 13:53:02 +00:00
claude commented 2026-03-23 15:26:16 +00:00
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PR created: #1089

What was done

vllm-mlx is now a first-class provider type in the cascade router.

Code changes

  • cascade.py: Added vllm_mlx provider type with:
    • Availability check via /health endpoint (strips /v1 suffix before calling)
    • _call_vllm_mlx() using the OpenAI async client pointed at the local server
    • Not subject to the metabolic-protocol cloud-skip (it is local)
  • providers.yaml: Added vllm-mlx-local entry (disabled by default, priority 2) with setup instructions
  • 7 new unit tests covering health checks, URL normalisation, completion routing, and metabolic-protocol bypass

Evaluation findings (from issue data)

Metric vllm-mlx vs Ollama
Throughput +21–87% over llama.cpp; +38–59% on M3 Ultra
Memory ~15% lower
Tool calling Identical (OpenAI-compatible API)

Recommendation

Use vllm-mlx when throughput is the priority on Apple Silicon. Stay on Ollama for broadest ecosystem compatibility and simpler setup. Enable by starting the vllm-mlx server and setting enabled: true in providers.yaml.

PR created: #1089 ## What was done **vllm-mlx is now a first-class provider type in the cascade router.** ### Code changes - `cascade.py`: Added `vllm_mlx` provider type with: - Availability check via `/health` endpoint (strips `/v1` suffix before calling) - `_call_vllm_mlx()` using the OpenAI async client pointed at the local server - Not subject to the metabolic-protocol cloud-skip (it is local) - `providers.yaml`: Added `vllm-mlx-local` entry (disabled by default, priority 2) with setup instructions - 7 new unit tests covering health checks, URL normalisation, completion routing, and metabolic-protocol bypass ### Evaluation findings (from issue data) | Metric | vllm-mlx vs Ollama | |---|---| | Throughput | +21–87% over llama.cpp; +38–59% on M3 Ultra | | Memory | ~15% lower | | Tool calling | Identical (OpenAI-compatible API) | ### Recommendation **Use vllm-mlx when throughput is the priority on Apple Silicon.** Stay on Ollama for broadest ecosystem compatibility and simpler setup. Enable by starting the vllm-mlx server and setting `enabled: true` in `providers.yaml`.
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Reference: Rockachopa/Timmy-time-dashboard#1069
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