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Can the AMD Radeon RX 7600 XT run Magistral Small 2506?

57Good quality (B) · 57 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (tight fit)

Runs well (tight fit). At Q3_K_M it needs about 14.3 GB of 16.0 GB usable, with an estimated speed of 16.7 tok/s.

Best quant
Q3_K_M
Generation speed
16.7 tok/s
Prompt processing
1,186 tok/s
First token (1K prompt)
0.9 s
Largest safe context
18K

Best app to run it on the AMD Radeon RX 7600 XT

On Windows

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

  • AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.
lemonade pull unsloth/Magistral-Small-2506-GGUF:Q3_K_M
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama One command with Ollama’s own build of this model.

On Linux

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

  • AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.
lemonade pull unsloth/Magistral-Small-2506-GGUF:Q3_K_M
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama One command with Ollama’s own build of this model.

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Magistral Small 2506

Recommended for the AMD Radeon RX 7600 XT: Q3_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run magistral:24b

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q3_K_M file (opens in a new tab)

huggingface.co/unsloth/Magistral-Small-2506-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Magistral-Small-2506-GGUF:Q3_K_M

Original weights · Hugging Face

Full-precision files from Mistral AI, for fine-tuning, vLLM, Transformers.

huggingface.co/mistralai/Magistral-Small-2506 (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 57Good quality (B) · 57 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q3_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs welltight fit
How well it runs on the AMD Radeon RX 7600 XT: speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.25.7 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.24.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.9.7 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score74.4 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the AMD Radeon RX 7600 XT

Quant Memory needed Available Speed Runs Quality Download
Q2_K 12.2 GB 16.0 GB 21.2 tok/s Runs well 47Fair quality (C) · 47 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 14.3 GB 16.0 GB 16.7 tok/s Runs welltight fit 57Good quality (B) · 57 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 17.1 GB 16.0 GB 10.7 tok/s Runs welloffloads to RAM 64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 19.5 GB 16.0 GB 6.8 tok/s Slowoffloads to RAM 65Good quality (B) · 65 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 22.1 GB 16.0 GB 4.8 tok/s Slowheavy RAM offload 67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 27.7 GB 16.0 GB 2.9 tok/s Very slowheavy RAM offload 68Good quality (B) · 68 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 49.9 GB 16.0 GB — Won't run 68Good quality (B) · 68 of 100: how capable the model is at F16, whatever it runs on. Match score 9/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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