FITMYLLM · AUGUST 17, 2026 · OPEN METHODOLOGY · COMMUNITY BENCHMARKS
LIVE · UPDATED DAILY
Quantization Options Select your GPU for speed estimates Quant Bits VRAM @ 8K Quality IQ2_M 2.93 27.6 GB
26.3 + 1.3 KV
low Q2_K 3.16 29.6 GB
28.4 + 1.3 KV
low IQ3_XXS 3.25 30.4 GB
29.2 + 1.3 KV
low IQ3_XS 3.5 32.6 GB
31.4 + 1.3 KV
low Q3_K_S 3.64 33.9 GB
32.6 + 1.3 KV
low IQ3_M 3.76 34.9 GB
33.7 + 1.3 KV
low Q3_K_M 4 37.0 GB
35.8 + 1.3 KV
low Q3_K_L 4.3 39.7 GB
38.4 + 1.3 KV
moderate IQ4_XS 4.46 41.1 GB
39.8 + 1.3 KV
moderate Q4_K_S 4.67 43.0 GB
41.7 + 1.3 KV
moderate Q4_K_M 4.89 44.9 GB
43.6 + 1.3 KV
good Q5_K_S 5.57 50.9 GB
49.6 + 1.3 KV
good Q5_K_M 5.7 52.0 GB
50.8 + 1.3 KV
good Q6_K 6.56 59.6 GB
58.4 + 1.3 KV
excellent Q8_0 8.5 76.8 GB
75.5 + 1.3 KV
lossless FP16 16 142.9 GB
141.7 + 1.3 KV
lossless
Select your GPU above to see speed estimates and compatibility for each quantization.
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Community Ratings Chat Coding Reasoning Creative Vision Roleplay Agentic
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Run this model IQ2_M — 26.3 GB VRAM Q2_K — 28.4 GB VRAM IQ3_XXS — 29.2 GB VRAM IQ3_XS — 31.4 GB VRAM Q3_K_S — 32.6 GB VRAM IQ3_M — 33.7 GB VRAM Q3_K_M — 35.8 GB VRAM Q3_K_L — 38.4 GB VRAM IQ4_XS — 39.8 GB VRAM Q4_K_S — 41.7 GB VRAM Q4_K_M — 43.6 GB VRAM Q5_K_S — 49.6 GB VRAM Q5_K_M — 50.8 GB VRAM Q6_K — 58.4 GB VRAM Q8_0 — 75.5 GB VRAM FP16 — 141.7 GB VRAM
Ollama llama.cpp vLLM LM Studio KoboldCpp Jan Docker
▸ Easiest way to get started · Beginners
DOCS ↗ curl -fsSL https://ollama.com/install.sh | shCOPY
$ ollama run llama3:70b-instruct-q4_K_MCOPY
Downloads and runs automatically. Add --verbose for speed stats.
▸ SETUP GUIDE >_
Auto-setup with fitmyllm CLI Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.
Auto-detect GPU Live tok/s in chat Speed benchmarks 9 inference engines
GPUs that can run this model At Q4_K_M quantization. Sorted by minimum VRAM.
Find the best GPU for Llama 3 70B
Build Hardware for Llama 3 70B Llama 3 70B — top-tier open model with strong coding and reasoning.
Read full model card ▸ COLOPHON FITMYLLM · INDEPENDENT · DATA-DRIVEN
FITMYLLM · EST. 2025 · © 2026
RECOMMENDATIONS FROM PUBLISHED MATH, CORRECTED BY THE COMMUNITY — 30.
▸ SPEC SHEET
Llama 3 70B — 70.6B Dense. ▸ SPECIFICATIONS
PARAMETERS 70.6B
ARCHITECTURE Dense Transformer
CONTEXT LENGTH 8K tokens
CAPABILITIES chat, coding
RELEASE DATE 2024-04-18
PROVIDER Meta
FAMILY llama ▸ VRAM REQUIREMENTS
QUANT BPW VRAM QUALITY IQ2_M 2.93 26.3 GB 75% Q2_K 3.16 28.4 GB 78% IQ3_XXS 3.25 29.2 GB 82% IQ3_XS 3.5 31.4 GB 84% Q3_K_S 3.64 32.6 GB 85% IQ3_M 3.76 33.7 GB 86% Q3_K_M 4 35.8 GB 88% Q3_K_L 4.3 38.4 GB 90% IQ4_XS 4.46 39.8 GB 92% Q4_K_S 4.67 41.7 GB 93% Q4_K_M 4.89 43.6 GB 94% Q5_K_S 5.57 49.6 GB 96% Q5_K_M 5.7 50.8 GB 96% Q6_K 6.56 58.4 GB 97% Q8_0 8.5 75.5 GB 100% FP16 16 141.7 GB 100%
§ 01 BENCHMARK SCORES
HumanEval 72.0
MMLU-PRO 55.0
MATH 50.4
IFEval 77.6
BBH 81.0
GPQA 39.5
MUSR 22.3
MBPP 69.0
Arena Elo 1222.0
LiveCodeBench 19.8
AIME 0.0
GPQA Diamond 37.9
HLE 4.4
§ 02 RUN COMMAND
Run Llama 3 70B locally with Ollama — needs 43.6 GB VRAM at Q4_K_M:
$ ollama run llama3:70b
§ 03 COMPATIBLE GPUs
30 @ Q4_K_M Feedback 