Code Llama VRAM Requirements: 7B, 13B, 34B and 70B Across Every Precision

Code Llama (Meta) ships in 7B, 13B, 34B and 70B parameter variants, plus Python-specialised and instruct-tuned forks. It was the first credible open-weight code model, and even now in 2026 plenty of IDE integrations and CI pipelines target it specifically. Sizing it for self-hosting is straightforward — the math is roughly the same as Llama 2 — but the long-context coding use case has KV cache implications worth a careful look.

Headline numbers

VRAM by Code Llama size

Code Llama 7B

Fits any modern GPU. INT4 (~5 GB) runs on an RTX 3050 6 GB; FP16 (~14 GB) needs a 16 GB+ card. Use cases: line-completion in IDEs, simple refactors, single-file code generation. Reference card: RTX 5060 8 GB at INT4 or RTX 5080 at FP16.

Code Llama 13B

The production sweet spot. ~26 GB FP16 fits a 32 GB card with room for KV cache; ~8 GB INT4 fits a 12 GB+ card. Use cases: production code-completion APIs, structured generation, code review automation.

Code Llama 34B

~68 GB FP16. Single-card options: RTX 6000 Pro 96 GB at FP16 or A100 80 GB. INT4 (~18 GB) fits a single RTX 5090. The 34B is meaningfully stronger than 13B on multi-file tasks but the hardware jump is significant — most teams skip directly from 13B to 70B if they outgrow.

Code Llama 70B

Same VRAM profile as Llama 3 70B. ~140 GB FP16 — multi-GPU only. ~40 GB INT4 — fits 2× RTX 5090. See can RTX 5090 run Llama? for the equivalent sizing on the Llama 3 family.

KV cache for long-context coding

Code Llama supports up to 100K context (16K natively, scaled with RoPE). Long context is genuinely useful for codebase Q&A but expensive in VRAM. Per-request KV cache:

Which GPU fits which variant

Real-world deployments

• IDE backend, 50 engineers — Code Llama 13B FP16 on a single RTX 5090. ~80 tok/s single-stream. £399/mo.
• Code review bot — Code Llama 34B AWQ-INT4 on a single RTX 5090. ~22 tok/s but quality matches the dense 34B. £399/mo.
• Internal codebase Q&A — Code Llama 70B INT4 on 2× RTX 5090. 32K context fits. £899/mo.
• Embedded device prototype — Code Llama 7B INT4 on RTX 3050 6 GB. ~25 tok/s. £79/mo.

Code Llama vs DeepSeek-Coder vs Codestral

Honestly, in 2026 most new code-specialised deployments don’t pick Code Llama. The two stronger open alternatives:

• DeepSeek-Coder 6.7B / 33B — usually outperforms Code Llama at similar parameter counts. See best GPU for DeepSeek.
• Codestral 22B — Mistral’s code model. Strong on multi-file Python, Apache 2.0.

Code Llama remains relevant when you need ecosystem compatibility — IDE plugins that target the Code Llama API shape, fine-tunes built on the original weights — or when you want a 70B-class code model and DeepSeek-V3 is too big for your hardware.