FITMYLLM · SEPTEMBER 6, 2026 · OPEN METHODOLOGY · COMMUNITY BENCHMARKS
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Quantization Options Select your GPU for speed estimates Context length: 4K 8K 16K 32K 64K 128K
Quant Bits VRAM @ 16K Quality IQ2_M 2.93 30.1 GB
26.3 + 3.8 KV
low Q2_K 3.16 32.1 GB
28.4 + 3.8 KV
low IQ3_XXS 3.25 32.9 GB
29.2 + 3.8 KV
low IQ3_XS 3.5 35.1 GB
31.4 + 3.8 KV
low Q3_K_S 3.64 36.4 GB
32.6 + 3.8 KV
low IQ3_M 3.76 37.4 GB
33.7 + 3.8 KV
low Q3_K_M 4 39.5 GB
35.8 + 3.8 KV
low Q3_K_L 4.3 42.2 GB
38.4 + 3.8 KV
moderate IQ4_XS 4.46 43.6 GB
39.8 + 3.8 KV
moderate Q4_K_S 4.67 45.5 GB
41.7 + 3.8 KV
moderate Q4_K_M 4.89 47.4 GB
43.6 + 3.8 KV
good Q5_K_S 5.57 53.4 GB
49.6 + 3.8 KV
good Q5_K_M 5.7 54.5 GB
50.8 + 3.8 KV
good Q6_K 6.56 62.1 GB
58.4 + 3.8 KV
excellent Q8_0 8.5 79.3 GB
75.5 + 3.8 KV
lossless FP16 16 145.4 GB
141.7 + 3.8 KV
lossless
Select your GPU above to see speed estimates and compatibility for each quantization.
Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise → ▸ READY TO RUN THIS? RENT BY THE HOUR
RENT A GPU AND RUN LLAMA 3.1 70B NOW
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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 llama3.1:70b-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 3.1 70B
Build Hardware for Llama 3.1 70B Llama 3.1 70B — near frontier quality. Strong at everything, 128K context.
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 3.1 70B — 70.6B Dense. ▸ SPECIFICATIONS
PARAMETERS 70.6B
ARCHITECTURE Dense Transformer
CONTEXT LENGTH 128K tokens
CAPABILITIES chat, coding, reasoning, tool_use
RELEASE DATE 2024-07-23
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 80.5
MMLU-PRO 53.8
MATH 68.0
IFEval 87.5
BBH 55.3
GPQA 46.7
MUSR 22.8
BigCodeBench 40.1
Arena Elo 1487.0
GPQA Diamond 49.1
LiveCodeBench 26.9
AIME 11.3
HLE 3.6
AA Intelligence 12.6
AA Coding 9.2
AA Math 11.3
aa_ifbench 29.0
aa_tau2 21.6
aa_scicode 27.7
aa_lcr 2.0
MATH-500 4.0
§ 02 RUN COMMAND
Run Llama 3.1 70B locally with Ollama — needs 43.6 GB VRAM at Q4_K_M:
$ ollama run llama3.1:70b
§ 03 COMPATIBLE GPUs
30 @ Q4_K_M Feedback 