Synced from Hugging Face — 2026-10-28

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)

Hardware this fits

RTX 4060 Ti 16GB

16 GB VRAM

RTX 4090 24GB

24 GB VRAM

RTX 5090 32GB

32 GB VRAM

VRAM calculator

Estimate total VRAM for a given model size, quantization, and context length. Estimates only — see the note at the bottom.

Weight size source

Model size: 7B

1B30B60B90B120B

Quantization

Context length: 4,096 tokens

5124K32K256K

Inference engine

llama.cpp vLLM

Total required VRAM

Model weights: —
KV cache: —
Runtime overhead (10%): —
Total VRAM: —
GPU VRAM needed (total ÷ 0.90): —

Live changes

No changes yet — adjust a control above.

Generic estimate: weight size is bits-per-weight × parameter count; KV cache uses the reference architecture nearest to the size you enter (7B/13B/32B/70B: real published models; 120B: extrapolated, no real model exists at that exact size). Sizes between those references are labeled Rough estimate and assume a dense model, so use a model-specific page for an exact figure. Model-specific (HF-synced): weight size is the exact .gguf file size fetched from Hugging Face — no estimation. KV cache is still estimated. For models whose published attention config is recorded on this site (layers, KV heads, head dimension, and any sliding-window layout), it is computed from that config. For every other model the calculator falls back to the nearest reference bucket, but only when the model is within 5B parameters of a bucket and is not a mixture-of-experts model; otherwise no total is shown. Batch size is fixed at 1 in both modes. Multiple GPUs: pooled VRAM is the number of cards times the VRAM per card, compared against the same total (weights + KV cache + 10% overhead). Engines: llama.cpp reserves the KV cache for the full context when the model loads. vLLM reserves 90% of VRAM by default (so the GPU needs the total divided by 0.90) and allocates KV in 16-token blocks; its overhead reuses the same 10% estimate, and it usually serves safetensors rather than GGUF. Fit status: offload assumes about 4 GB of system RAM stays with the operating system. Your GPU: choosing a card uses its VRAM. For Apple Silicon the GPU can use about 75% of unified memory by default, with no separate system RAM to offload into. Treat all figures here as a starting estimate, not a guarantee. Full formula and known limitations: /methodology.

Deploy this locally

A minimal Docker Compose stack to get Ollama and Open WebUI running. Free to copy and run as-is.

services:
  ollama:
    image: ollama/ollama:latest
    container_name: ollama
    restart: unless-stopped
    ports:
      - "11434:11434"
    volumes:
      - ollama-data:/root/.ollama

  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open-webui
    restart: unless-stopped
    depends_on:
      - ollama
    ports:
      - "3000:8080"
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
    volumes:
      - open-webui-data:/app/backend/data

volumes:
  ollama-data:
  open-webui-data:

Production-ready

Local Production Node Docker Stack — $35

The free snippet above gets you running. This is what you'd actually want in production.

Hardened multi-model compose (run several models side by side)
Automated container health checks and restart policies
Reverse proxy pre-configured for all services
Caddy with automatic SSL — no manual cert management
Vector storage wired in for RAG workloads
Automated logging and log rotation

Get the production stack — $35

One-time purchase via Lemon Squeezy. Single-user license. 7-day refund window — see Terms.

FAQ

How is VRAM calculated for Meta-Llama-3-8B-Instruct-GGUF?

Total VRAM combines three things: the model weight size (exact for synced quantizations, from the real .gguf file), the KV cache size (always estimated — see /methodology for why), and a runtime overhead margin of 10% of (weights + KV cache), a rule-of-thumb allowance for CUDA context and activation buffers rather than a fixed number, since it scales with model size.

Why does context length drastically change VRAM requirements?

The KV cache stores attention history so the model doesn't recompute earlier tokens on every step. KV cache size scales linearly with context length, so doubling context roughly doubles that portion of VRAM use. On larger models this can add many gigabytes as context grows from 4K toward 32K or 128K tokens — see the calculator above to see the real numbers for Meta-Llama-3-8B-Instruct-GGUF at different context lengths.

What happens if my GPU doesn't have enough VRAM for Meta-Llama-3-8B-Instruct-GGUF?

Runners like llama.cpp or Ollama can offload remaining layers to system RAM over the PCIe bus. Execution still works, but system RAM bandwidth (roughly 60–80 GB/s for dual-channel DDR5) is far slower than GPU VRAM (roughly 1,000 GB/s), so generation speed typically drops sharply — often from tens of tokens per second down to single digits — the more layers get offloaded.

How can I fit a model into limited VRAM?

A few real options: drop to a smaller quantization (e.g. Q4_K_M instead of Q8_0), reduce context length (KV cache scales with it directly), or quantize the KV cache itself (llama.cpp's --cache-type-k q8_0 --cache-type-v q8_0) to roughly halve KV cache memory. That last option is a real tradeoff, not free — it can introduce a small amount of quality degradation, generally minor for most use cases but worth testing on your own workload before relying on it.