Budget Pick
NVIDIA GeForce RTX 409024 GB VRAM · ~40.3 tok/s
Lowest cost that meets recommended VRAM
Rent on RunPodCompatibility Check
CodeLlama 34B is a 34B parameter model from the CodeLlama family. Check if your hardware can handle it.
Send this page to a friend or teammate so they can check whether CodeLlama 34B fits their hardware too.
Social proof
32% of 1,596 scanned PCs run CodeLlama 34B fully on GPU.
962 keep at least some work on GPU. Based on anonymous compatibility checks.
Beginner tip: minimum values mean the model can start, while recommended values usually feel smoother during real use. VRAM is your GPU's dedicated memory; RAM is your system memory used as fallback. See the full glossary.
| Quantization | File Size | Min VRAM | Recommended VRAM | Min RAM | Context |
|---|---|---|---|---|---|
| Q4_K_MEasiest | 20 GB | 22 GB | 24 GB | 24 GB | 4K / 16K |
Not sure your GPU has enough VRAM? Compare GPUs that can run CodeLlama 34B.
These GPUs meet the recommended 24 GB VRAM for the Q4_K_M quantization. Estimated speeds are approximate and assume full GPU offloading.
Budget Pick
NVIDIA GeForce RTX 409024 GB VRAM · ~40.3 tok/s
Lowest cost that meets recommended VRAM
Rent on RunPodFastest Pick
NVIDIA GeForce RTX 509032 GB VRAM · ~71.7 tok/s
Highest estimated throughput
Check price on AmazonBest Value
NVIDIA GeForce RTX 3090 Ti24 GB VRAM · ~40.3 tok/s
Best speed per dollar of VRAM
Check price on AmazonNeed a detailed comparison? See all GPU rankings for CodeLlama 34B.
Strong OpenClaw Model Candidate
CodeLlama 34B is a common OpenClaw pick for local agent workflows. Use this model with Ollama, llama.cpp, or LM Studio, then confirm full OpenClaw hardware compatibility.
Why choose CodeLlama 34B?
General-purpose local model brief
Quantization tip: Benchmark at least two quantizations and validate with a task-specific eval set before production use.