standardLlama 3 Community License8B paramsOpen weights

Llama 3 8B

8B parameter open-weight model. Widely supported across runtimes and tools. GQA reduces KV cache vs older Llama 2. Good baseline for chat and coding on 8 GB VRAM.

Meta · Llama 3

Model overview

Llama 3 8B is Meta's original Llama 3 checkpoint, the direct predecessor to the more widely used Llama 3.1 revision. It's a solid, well-tested option for chat, coding help, and light RAG work, and its grouped-query attention keeps KV cache usage manageable at longer prompts compared to older Llama 2 models. Its 8K context window is shorter than most current-generation 8B models, so it's best suited to shorter conversations and single-document tasks rather than long-document summarization. Because it shipped early and has been widely adopted, community support, fine-tunes, and troubleshooting guidance for it are unusually mature.

Check your GPU fitWhat is VRAM?What is quantization?Local LLM quickstart

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedJune 2026SourcesHuggingFace model card (meta-llama/Meta-Llama-3-8B-Instruct), official docs, OpenSourcesAI editorial review.

VRAM figures are empirical estimates. Actual usage varies by runtime, context length, and system configuration. Verify on your specific hardware before production use.

Will Llama 3 8B run on your machine?

Llama 3 8B is 8B parameters and needs 7.1 GB of VRAM at Q4_K_M — 5.6 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.

VRAM by quantization

QuantizationWeightsNeeds (with overhead)Quality
Q4_K_S5.2 GB6.7 GBgood
Q4_K_M5.6 GB7.1 GBgood
Q8_09.5 GB11 GBhigh
FP1616 GB17.5 GBreference

Fit on common hardware at Q4_K_M

HardwareMemory the model can useSystem RAMVerdict
CPU OnlyNone (CPU only)16 GBCPU offload
RTX 4060 Laptop8 GB16 GBTight
RTX 3060 (12GB)12 GB32 GBComfortable
RTX 4060 Ti (16GB)16 GB32 GBComfortable
RTX 309024 GB64 GBComfortable

Comfortable means VRAM clears the requirement by 2 GB or more. Tight means it covers the requirement with no margin. CPU offload means the model does not fit in VRAM but system RAM is at least 1.6× the weights, so it will run at reduced speed — expect roughly 1–5 tokens per second. Figures are weights plus a fixed runtime overhead and exclude KV-cache growth, which scales with context length.

Check Llama 3 8B against your exact hardwareHow these verdicts are calculated

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Related models

DeepSeek-R1 8B (0528 Qwen3)8B parameter model — shared focus: reasoning, coding, chat.Qwythos-9B (Claude-Mythos-5 1M)9B parameter model — shared focus: reasoning, coding, tool-use.Qwen3.5 9B9.7B parameter model — shared focus: chat, reasoning, long-context.Llama 3.1 405B InstructServer-class assistant evaluation and comparisons against smaller Llama variants.

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