CodeLlama 34B: Hardware & Business Fit
- Code
The largest CodeLlama, suitable for review and refactoring. Newer 32B-class open coders generally beat it now; included for completeness of the family.
- Parameters
- ~34B
- Context
- ~16K tokens
- Deployment
- hybrid
- VRAM @ 4-bit
- ~21GB
What CodeLlama 34B is good for
- ▸Code review & refactoring
- ▸Repo-aware assistance
- ▸Migration help
Best quantization choices
Approximate memory per quantization (weights + KV cache at modest context). Treat as ±.
| Quant | ~Memory | When to use |
|---|---|---|
| Q4_K_M | ~21GB | Best size/quality trade-off — the usual default for local serving. |
| Q8_0 | ~37GB | Higher fidelity; ~1.7× the memory of 4-bit. |
| FP16 | ~68GB | Full precision; largest footprint, best quality. |
Run CodeLlama 34B locally
Pull and run with Ollama, or grab the weights from Hugging Face.
$ ollama run codellama:34bmeta-llama/CodeLlama-34b-Instruct-hfCompatible hardware
Devices from our catalog graded for CodeLlama 34B, best fit first.
- NVIDIA B200 (placeholder)NVIDIA · Datacenter GPUs
Fits at FP16 (~68GB) with ~101GB headroom — about 2 concurrent instances.
FP16 · ~68GBRuns well - Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~68GB) with ~495.2GB headroom — about 8 concurrent instances.
FP16 · ~68GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~68GB) with ~495.2GB headroom — about 8 concurrent instances.
FP16 · ~68GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~68GB) with ~101GB headroom — about 2 concurrent instances.
FP16 · ~68GBRuns well - Cloud B200 (Blackwell profile, to verify)Cloud · Cloud GPU Profiles
Fits at FP16 (~68GB) with ~90.4GB headroom — about 2 concurrent instances.
FP16 · ~68GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~68GB) with ~56.1GB headroom — about 1 concurrent instance.
FP16 · ~68GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~68GB) with ~56.1GB headroom — about 1 concurrent instance.
FP16 · ~68GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~68GB) with ~2.4GB headroom — about 1 concurrent instance.
FP16 · ~68GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~68GB) with ~2.4GB headroom — about 1 concurrent instance.
FP16 · ~68GBRuns well - NVIDIA RTX PRO 6000 BlackwellNVIDIA · Professional GPUs
Fits at FP16 (~68GB) with ~16.5GB headroom — about 1 concurrent instance.
FP16 · ~68GBRuns well
Use inside the AI Business OS
CodeLlama 34B suits these AI Business OS agent archetypes:
A model is only the engine. Inside the AI Business OS it is wrapped with permissions, tools, connectors, RAG and audit so it can actually do business work safely — see how the AI Business OS works →
Frequently asked questions
What hardware do I need to run CodeLlama 34B?+
At 4-bit you need roughly ~21GB of usable memory. The minimum self-hostable option in our catalog is the NVIDIA GeForce RTX 3090. For a comfortable run we recommend the NVIDIA B200 (placeholder).
Which quantization should I use for CodeLlama 34B?+
Q4_K_M is the usual default — the best size/quality trade-off. Step up to Q8_0 or FP16 if you have spare memory and want higher fidelity.
Should I run CodeLlama 34B locally or in the cloud?+
Hybrid is recommended for CodeLlama 34B. Run it locally where it fits and burst to the cloud for peaks or larger jobs.
Other sizes in the CodeLlama family
All CodeLlama models →Same family, different size. Pick the variant that fits your hardware.
Related models
Similar picks — family siblings and nearest-size models of the same kind.
Use CodeLlama 34B inside your AI Business OS
BrainOutput helps you run CodeLlama 34B as a private business agent — wrapped with the tools, connectors, RAG and guardrails it needs to do real work on hardware you control.
Use this model in your AI Business OS