on

Can the AMD Radeon RX 7600 XT run Granite Code 34B Instruct?

28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well (tight fit)

Runs well (tight fit). At Q2_K it needs about 15.3 GB of 16.0 GB usable, with an estimated speed of 14.8 tok/s.

Best quant
Q2_K
Generation speed
14.8 tok/s
Prompt processing
829 tok/s
First token (1K prompt)
1.2 s
Largest safe context
8K

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 ibm-granite/granite-34b-code-instruct-8k-GGUF:Q2_K
  • 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 ibm-granite/granite-34b-code-instruct-8k-GGUF:Q2_K
  • 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? Granite Code 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 Granite Code 34B Instruct

Recommended for the AMD Radeon RX 7600 XT: Q2_K. 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 granite-code:34b-instruct-q2_K

Quantized files · GGUF

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

huggingface.co/ibm-granite/granite-34b-code-instruct-8k-GGUF (opens in a new tab)

ollama run hf.co/ibm-granite/granite-34b-code-instruct-8k-GGUF

Original weights · Hugging Face

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

huggingface.co/ibm-granite/granite-34b-code-instruct-8k (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 28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, 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.12.6 / 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.9.1 / 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 score60.7 / 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 Radeon RX 7600 XT

Quant Memory needed Available Speed Runs Quality Download
Q2_K 15.3 GB 16.0 GB 14.8 tok/s Runs welltight fit 28Basic quality (D) · 28 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.
Q3_K_M 18.3 GB 16.0 GB 8.5 tok/s Slowoffloads to RAM 33Basic quality (D) · 33 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 47/100 (quality, fit, speed and memory use) sets the ranking.
Q4_K_M 22.3 GB 16.0 GB 4.9 tok/s Slowheavy RAM offload 38Basic quality (D) · 38 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 41/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 25.7 GB 16.0 GB 3.5 tok/s Slowheavy RAM offload 38Basic quality (D) · 38 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 40/100 (quality, fit, speed and memory use) sets the ranking.
Q6_K 29.4 GB 16.0 GB 2.7 tok/s Very slowheavy RAM offload 39Basic quality (D) · 39 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 40/100 (quality, fit, speed and memory use) sets the ranking.
Q8_0 37.5 GB 16.0 GB — Won't run 40Fair quality (C) · 40 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking.
F16 69.2 GB 16.0 GB — Won't run 40Fair quality (C) · 40 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

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.

Was this page helpful?