I think I asked this question of an AI a couple months ago and tried KV Cache. I'd been mixing 2x3060s with an AMD 6900xt so this could work better with others with a full Cuda or ROCm setup.

"What are some Turboquant alternatives that could be better"

If you are looking for alternatives to TurboQuant, the options generally fall into two categories: high-speed research-grade methods for KV cache compression or established production standards for weight quantization.

1. Research-Grade Alternatives (KV Cache Focus)

Since TurboQuant is specifically optimized for KV cache memory during inference, your closest alternatives are other methods that target memory bottlenecks in long-context tasks.

• RotorQuant (or PlanarQuant): This is currently a top alternative for those prioritizing speed. It uses Clifford Algebra "rotors" instead of TurboQuant’s random orthogonal matrices.
• The Advantage: It can be 10–19x faster than standard matmuls and up to 30x faster on specific Metal shaders (Apple Silicon). It significantly reduces the parameter count needed for rotation, making it leaner than TurboQuant.
• The Tradeoff: It mixes information in smaller blocks (e.g., 3D groups), which can lead to higher error on "worst-case" vectors compared to TurboQuant’s global rotation.
• KIVI (2-bit KV Cache): If your goal is extreme memory savings to fit massive contexts, KIVI is a robust alternative. It uses a 2-bit quantization scheme that helps maintain accuracy by treating "outlier" tokens differently from the rest of the cache.
• Hadamard Transform Methods: Some recent implementations replace TurboQuant's Haar transform with a Walsh-Hadamard transform. This is often more efficient for extracting entropy and can be easier to implement in certain hardware kernels while offering similar "spreading" of energy across dimensions.

2. High-Performance Inference Engines

If your interest in TurboQuant is about maximizing "tokens per second" on high-parameter models like Qwen 3.5 or DeepSeek-R1, these engines often provide better alternatives to custom quantization scripts:

• ExLlamaV2 (EXL2): Widely considered the "gold standard" for raw throughput on NVIDIA hardware. It uses a unique quantization method that allows for non-integer bitrates (e.g., 3.5-bit or 4.25-bit), letting you fit a model precisely to your available VRAM while maintaining higher performance than GGUF.
• vLLM with Marlin Kernels: If you are running 4-bit AWQ or GPTQ models, using the Marlin kernel within vLLM can nearly double your throughput compared to standard kernels. It is highly optimized for high-density GPU compute.

3. Established Standard Alternatives

For general-purpose use where compatibility and stability are more important than experimental KV cache compression:

• GGUF (with Importance Matrix): While usually slower than EXL2, using GGUF with an "i-matrix" (Importance Matrix) often yields better perplexity (accuracy) than standard 4-bit quants. It is the best choice if you need to offload specific layers to a CPU or split workloads across mismatched GPUs.
• AWQ (Activation-aware Weight Quantization): This is typically better than GPTQ for instruction-following and coding tasks. It protects the most "salient" weights during the compression process, which results in fewer "hallucinations" or broken logic at lower bitrates.

Summary Comparison