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Can the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (64 GB) run Code Llama 34B Instruct?

31Basic quality (D) · 31 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.

Yes, but slowly

Slow. At Q3_K_M it needs about 22.6 GB of 48.0 GB usable, with an estimated speed of 3.6 tok/s.

Best quant
Q3_K_M
Generation speed
3.6 tok/s
Prompt processing
437 tok/s
First token (1K prompt)
2.3 s
Largest safe context
16K

Best app to run it on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (64 GB)

On Windows

Lemonade

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

  • Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
lemonade pull TheBloke/CodeLlama-34B-Instruct-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)

  • Built for Ryzen AI and Strix Halo, including NPU + iGPU modes on Windows.
lemonade pull TheBloke/CodeLlama-34B-Instruct-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.

Want an agent? Code Llama 34B Instruct supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 Code Llama 34B Instruct

Recommended for the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (64 GB): 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 codellama:34b-instruct-q3_K_M

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/TheBloke/CodeLlama-34B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/TheBloke/CodeLlama-34B-Instruct-GGUF:Q3_K_M

Original weights · Hugging Face

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

huggingface.co/meta-llama/CodeLlama-34b-Instruct-hf (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Meta approves it (usually within minutes to a few hours).

Official repo · GitHub

Reference code, inference scripts and docs from Meta. The weights themselves download from Hugging Face.

github.com/meta-llama/codellama (opens in a new tab)

Meta’s official Code Llama repo.

git clone https://github.com/meta-llama/codellama

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 31Basic quality (D) · 31 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 54/100 (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 Slow
How well it runs on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (64 GB): 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.14.4 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.4.5 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.10.9 / 15
Match score54.7 / 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 Ryzen AI 9 HX 370 (DDR5 SO-DIMM) (64 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 19.5 GB 48.0 GB 4.6 tok/s Slow 26Basic quality (D) · 26 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 22.6 GB 48.0 GB 3.6 tok/s Slow 31Basic quality (D) · 31 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 26.6 GB 48.0 GB 2.8 tok/s Very slow 36Basic quality (D) · 36 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 30.0 GB 48.0 GB 2.3 tok/s Very slow 36Basic quality (D) · 36 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 59/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 33.7 GB 48.0 GB 1.9 tok/s Very slow 37Basic quality (D) · 37 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 41.8 GB 48.0 GB 1.4 tok/s Very slowtight fit 38Basic quality (D) · 38 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 73.5 GB 48.0 GB — Won't run 38Basic quality (D) · 38 of 100: how capable the model is at F16, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. 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

Quick answers

How much memory does Code Llama 34B Instruct need on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB?

At Q3_K_M it needs about 22.6 GB. The AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB has 48.0 GB usable.

How fast is Code Llama 34B Instruct on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB?

About 3.6 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Code Llama 34B Instruct should I use on the AMD Ryzen AI 9 HX 370 (DDR5 SO-DIMM) 64GB?

Q3_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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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