mlx-community/Agents-A1-8bit

Agents-A1 — MLX (8-bit)

MLX 8-bit quantization of InternScience/Agents-A1 (affine, group size 64). The source is bf16; this is a uniform mlx quantization.

Agents-A1 is a Qwen3.5-MoE vision-language agent model (qwen3_5_moe, Qwen3_5MoeForConditionalGeneration): 40 decoder layers, 256 routed experts per layer + a shared expert, hidden size 2048, with a vision tower and video preprocessing.

Running it

Multimodal (VLM) — load with mlx-vlm (mlx-lm can't load multimodal architectures):

pip install mlx-vlm
python -m mlx_vlm.generate --model mlx-community/Agents-A1-8bit \
  --prompt "What is 17 * 24? Think step by step." --max-tokens 512
# with an image:
python -m mlx_vlm.generate --model mlx-community/Agents-A1-8bit --image img.jpg --prompt "Describe this image."

Loads and runs in stock mlx-vlm — no patched code needed at inference.

Conversion notes

I first tried oMLX's data-driven oQ quantization, but it doesn't work for this checkpoint: oQ writes the MoE experts in a per-expert layout that omlx's own loader can't read back (parameters not in model), so the quantized model fails to load. This build therefore uses standard mlx-vlm quantization instead — uniform 8-bit, group size 64 — which loads cleanly in both stock mlx-vlm & oMLX.

Throughput

Measured with oMLX's benchmark harness on a Macbook Pro M5 Max 128GB 40 GPU — gen 128 tokens, cold prefill (unique prompt prefix per request, no cache reuse).

Single request (batch 1) — decode tok/s by context

Context	bf16	8-bit	6-bit	5-bit	4-bit	3-bit
1,024	67.6	95.4	95.2	98.2	117.4	133.0
4,096	67.6	94.0	97.3	102.8	119.5	130.4
8,192	66.8	91.7	95.3	103.1	115.7	126.9
16,384	64.7	88.0	91.5	80.5	105.8	119.8
32,768	60.9	80.6	88.6	80.2	95.6	104.2
65,536	53.5	68.4	67.6	66.6	75.4	83.5
131,072	40.7	48.7	50.9	48.2	50.3	52.5
Peak RAM (GB)	66–69	35–39	27–31	23–26	19–22	15–18

TTFT (cold prefill) is ~precision-independent — ≈0.3 s @1k, 3 s @8k, 21 s @32k, 63 s @64k, ~225 s @128k — prefill is compute-bound, not weight-bound.

Continuous batching (1k context) — aggregate decode tok/s

Batch	bf16	8-bit	6-bit	5-bit	4-bit	3-bit
1	67.6	95.4	95.2	98.2	117.4	133.0
2	62.5	151.0	156.5	160.6	190.9	188.7
4	107.1	202.0	185.1	195.7	239.9	230.2
8	129.6	252.4	223.4	238.7	289.0	276.1

Aggregate across the batch; per-request rate is that value divided by the batch size.

Smoke test

17 x 24 -> correct (408), coherent, no repetition.

Other precisions

Precision	Repo	Size on disk
bf16 (full)	Agents-A1-bf16	~65 GB
8-bit	Agents-A1-8bit	~35 GB
6-bit	Agents-A1-6bit	~27 GB
5-bit	Agents-A1-5bit	~23 GB
4-bit	Agents-A1-4bit	~19 GB
3-bit	Agents-A1-3bit	~15 GB

License

apache-2.0, inherited from the base model.