Synced from Hugging Face — 2026-09-14
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need?
Exact on-disk weight sizes below are pulled directly from Hugging Face. Total VRAM figures also include an estimated KV cache and runtime overhead — see the breakdown per quantization, and the calculator below to adjust context length.
By quantization
Q2_K
2.96 GB exact, on disk
~3.81 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q3_K_L
4.03 GB exact, on disk
~4.98 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q3_K_M
3.74 GB exact, on disk
~4.66 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q3_K_S
3.41 GB exact, on disk
~4.30 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q4_0
4.34 GB exact, on disk
~5.32 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q4_1
4.78 GB exact, on disk
~5.81 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q4_K_M
4.58 GB exact, on disk
~5.59 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q4_K_S
4.37 GB exact, on disk
~5.36 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q5_0
5.21 GB exact, on disk
~6.28 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q5_1
5.65 GB exact, on disk
~6.77 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q5_K_M
5.34 GB exact, on disk
~6.42 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q5_K_S
5.21 GB exact, on disk
~6.28 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q6_K
6.14 GB exact, on disk
~7.30 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
Q8_0
7.95 GB exact, on disk
~9.29 GB total estimated VRAM at 4,096 tokens context (KV cache + overhead estimated, not exact)
VRAM calculator
Estimate total VRAM for a given model size, quantization, and context length. Estimates only — see the note at the bottom.
- Model weights
- —
- KV cache
- —
- Runtime overhead (10%)
- —
- Total VRAM
- —
Fits on:
This won't fit on local hardware
Based on the estimate above, this configuration exceeds every local GPU profile listed. Renting cloud GPU capacity is the practical option for a workload this size.
RunPod
On-demand GPU pods, billed by the minute.
Lambda Labs
Reserved and on-demand cloud GPU instances.
Disclosure: We may earn a commission from cloud providers if you spin up an instance through these links, at no extra cost to you. This never affects the VRAM math above — see /methodology.
Live changes
- No changes yet — adjust a control above.
Generic estimate: weight size
is bits-per-weight × parameter count; KV cache uses a reference
architecture for the selected size bucket (7B/13B/70B: real published
models; 120B: extrapolated, no real model exists at that exact
size). Model-specific (HF-synced):
weight size is the exact .gguf file size fetched from
Hugging Face — no estimation. KV cache is still estimated:
this schema doesn't carry layer count, head count, or head dimension,
so the calculator parses an approximate parameter count from the
model's title (e.g. "8B") and reuses the nearest size bucket's
reference architecture for the KV math, same caveats as the generic
mode. If no parameter count can be parsed from the title, it falls
back to the 7B architecture and says so next to the quantization
dropdown. Batch size is fixed at 1 in both modes. Treat all figures
here as a starting estimate, not a guarantee. Full formula and known
limitations: /methodology.
FAQ
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q2_K?
The Q2_K quantization is 2.96 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 3.81 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q3_K_L?
The Q3_K_L quantization is 4.03 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 4.98 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q3_K_M?
The Q3_K_M quantization is 3.74 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 4.66 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q3_K_S?
The Q3_K_S quantization is 3.41 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 4.30 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q4_0?
The Q4_0 quantization is 4.34 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 5.32 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q4_1?
The Q4_1 quantization is 4.78 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 5.81 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q4_K_M?
The Q4_K_M quantization is 4.58 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 5.59 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q4_K_S?
The Q4_K_S quantization is 4.37 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 5.36 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q5_0?
The Q5_0 quantization is 5.21 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 6.28 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q5_1?
The Q5_1 quantization is 5.65 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 6.77 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q5_K_M?
The Q5_K_M quantization is 5.34 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 6.42 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q5_K_S?
The Q5_K_S quantization is 5.21 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 6.28 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q6_K?
The Q6_K quantization is 6.14 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 7.30 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.
How much VRAM does Meta-Llama-3-8B-Instruct-GGUF need at Q8_0?
The Q8_0 quantization is 7.95 GB on disk (exact, synced from Hugging Face). At a 4,096-token context, using the 7B reference architecture for the KV cache estimate, total estimated VRAM is approximately 9.29 GB. This total includes a KV cache estimate and a 10% runtime overhead margin — only the weight size is exact.