Q: What quantization of CodeLlama 13B should I use on a NVIDIA GeForce GTX 1060 6GB?

For 6 GB VRAM on the NVIDIA GeForce GTX 1060 6GB, the Q4_K_M variant is the best fit. Estimated ~7 tokens/sec on the Q4_K_M quantization.

Q: How fast does CodeLlama 13B run on NVIDIA GeForce GTX 1060 6GB?

Roughly 7 tokens/sec for Q4_K_M. Real speed depends on context length, backend (Ollama, llama.cpp, LM Studio), and KV cache size.

Q: What if NVIDIA GeForce GTX 1060 6GB is not enough for CodeLlama 13B?

Consider upgrading to NVIDIA GeForce RTX 5070 (12 GB VRAM) which fits the recommended 12 GB target. Or pick a smaller quantization to stay on your current card.

Question 1

Can I run CodeLlama 13B on a NVIDIA GeForce GTX 1060 6GB?

Accepted Answer

Sort of — NVIDIA GeForce GTX 1060 6GB can run CodeLlama 13B (Q4_K_M) only by spilling layers to RAM. Generation will be slow. CPU + GPU hybrid — not enough VRAM (6 GB < 9 GB min), but 64 GB RAM is sufficient. Expect significantly slower inference.

Question 2

What quantization of CodeLlama 13B should I use on a NVIDIA GeForce GTX 1060 6GB?

Accepted Answer

For 6 GB VRAM on the NVIDIA GeForce GTX 1060 6GB, the Q4_K_M variant is the best fit. Estimated ~7 tokens/sec on the Q4_K_M quantization.

Question 3

How fast does CodeLlama 13B run on NVIDIA GeForce GTX 1060 6GB?

Accepted Answer

Roughly 7 tokens/sec for Q4_K_M. Real speed depends on context length, backend (Ollama, llama.cpp, LM Studio), and KV cache size.

Question 4

What if NVIDIA GeForce GTX 1060 6GB is not enough for CodeLlama 13B?

Accepted Answer

Consider upgrading to NVIDIA GeForce RTX 5070 (12 GB VRAM) which fits the recommended 12 GB target. Or pick a smaller quantization to stay on your current card.

Can I Run CodeLlama 13B on NVIDIA GeForce GTX 1060 6GB?

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Every CodeLlama 13B quantization on NVIDIA GeForce GTX 1060 6GB

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