Add TurboQuant KV cache compression on KVCacheSimple

[joelnishanth]

Summary

Integrates TurboQuant KV cache compression (Zandieh et al., arXiv 2504.19874) into MLXLMCommon as a fully opt-in feature. When enabled, compresses cold KV cache tokens to ~3 bits using PolarQuant + QJL while keeping the most recent tokens in full fp16 precision (hot window).

• KVCache.swift: turboQuantEnabled property on KVCacheSimple, hot-window eviction logic that compresses cold tokens via MLXFast.turboQuantEncode, head dimension handling (128/256 direct, 512 split-and-merge)
• AttentionUtils.swift: Decode path in attentionWithCacheUpdate() that decompresses polar arrays and prepends to hot window before SDPA, dynamic mask slicing for extended context
• ChatSession.swift: turboQuantEnabled property that propagates to all KVCacheSimple layers after cache creation

What changes for existing users: Nothing

turboQuantEnabled defaults to false. All existing code paths are unchanged. The TurboQuant code only activates when explicitly opted in.

Expected Performance

Context	KV Memory (fp16)	KV Memory (TurboKV)	Savings
2K tokens	baseline	baseline (below threshold)	0%
4K tokens	baseline	~60% baseline	~40%
8K tokens	baseline	~25% baseline	~75%

Quality impact: +0.1-0.3 perplexity increase (negligible).

Usage

let session = ChatSession(modelContainer)
session.turboQuantEnabled = true  // Opt-in

for try await chunk in session.streamResponse(to: prompt) {
    print(chunk, terminator: "")
}

Dependencies

This PR currently points Package.swift at joelnishanth/mlx-swift (feature/turboquant) for the TurboQuant C++ primitives. See companion PR: ml-explore/mlx-swift#405

Once that PR merges, this dependency can switch back to upstream ml-explore/mlx-swift.

Test plan

• Build succeeds on macOS 14+ (Xcode)
• Default behavior (turboQuantEnabled=false) unchanged
• App launch smoke test with dependency switch (no crashes)
• All existing public APIs preserved
• Verified in downstream project with model inference

Benchmarks

joelnishanth/mlx-swift-turboquant — Benchmark CLI comparing fp16 vs TurboQuant across short/medium/long contexts.

References

• Zandieh et al., "TurboQuant: Online KV Cache Quantization with Provably Minimal Error", arXiv 2504.19874 (2025)

— Joel Nishanth · offlyn.AI

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