ChatCodingAI CodingReasoningMulti-Purpose70.6B
Llama 3.1 70B Instruct
Llama · Llama 3.1 Community License
Frontier-class open model. Approaches GPT-4 quality on many benchmarks. Requires significant VRAM — 48GB+ recommended for usable quantizations. Excellent for serious local deployment.
- Parameters
- 70.6B
- Context
- 131,072 tokens
- Released
- 2024-07-23
- Engines
- llama.cpp, ollama, vLLM
- Builder Tools
- Cursor, Open WebUI
Llama 3.1 70B Instruct (70.6B) requires 47 GB VRAM at recommended quality (Q5_K_M). On NVIDIA GeForce RTX 5090, expect approximately 14 tok/s at Q4_K_M. For the best experience, High-End Home AI Server ($3,842) is recommended.
— OwnRig methodology, data updated 2026-03-01
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| recommended | Q5_K_M | 47 GB | 42.4 GB |
| efficient | Q4_K_M | 39.5 GB | 35.3 GB |
| compressed | Q3_K_M | 31.6 GB | 27.5 GB |
| compressed | Q2_K | 24.5 GB | 21.2 GB |
Context Length Impact
KV cache VRAM at Q5_K_M quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 614 MB | 47.6 GBexceeds 24 GB |
| 4K | 1.3 GB | 48.3 GBexceeds 24 GB |
| 8K | 2.6 GB | 49.6 GBexceeds 24 GB |
| 16K | 5.1 GB | 52.1 GBexceeds 24 GB |
| 32K | 10.2 GB | 57.2 GBexceeds 24 GB |
| 64K | 20.5 GB | 67.5 GBexceeds 24 GB |
| 128K | 41 GB | 88 GBexceeds 24 GB |
Compatible GPUs
Performance data for Llama 3.1 70B Instruct across different hardware.
| Device | Quantization | Speed | Rating | Fits in VRAM |
|---|---|---|---|---|
| NVIDIA GeForce RTX 4090 | Q3_K_M | 5 tok/s | Marginal | ✗ (offload) |
| Apple M4 Max (64GB Unified) | Q4_K_M | 8 tok/s | Acceptable | ✓ |
| Apple M4 Max (128GB Unified) | Q5_K_M | 7 tok/s | Acceptable | ✓ |
| NVIDIA GeForce RTX 5090 | Q4_K_M | 14 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 4060 8GB | Q2_K | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q2_K | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 3080 10GB | Q2_K | — | Not Viable | ✗ (offload) |
| Apple M3 Pro (18GB Unified) | Q2_K | — | Not Viable | ✗ (offload) |
Builder Context
Llama 3.1 70B Instruct is commonly used with Cursor, Open WebUI. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Recommended Builds
Complete PC builds that can run Llama 3.1 70B Instruct.
High-EndExtreme
High-End
High-End
High-EndFull AI Builder
AMD AI Powerhouse
AMD Radeon RX 7900 XTX 24GB · 32GB DDR5-5600 (2x16GB)
$1,81824 GB VRAM
Runs 7 models
Extreme
Extreme AI Workstation
2x NVIDIA GeForce RTX 3090 (Used) · 128GB DDR5-5600 (4x32GB)
$4,17148 GB VRAM
Runs 8 models
High-EndHome AI Server
High-End Home AI Server
2x NVIDIA GeForce RTX 3090 24GB (Used) + NVLink Bridge · 128GB DDR5-5600 (4x32GB)
$3,84248 GB VRAM
Runs 12 models
High-EndFull AI Builder
Mac Studio AI Builder
Apple M4 Max 128GB (Mac Studio)
$3,999128 GB VRAM
Runs 6 models
Frequently Asked Questions
- How much VRAM does Llama 3.1 70B Instruct need?
- Llama 3.1 70B Instruct requires 47 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 24.5 GB.
- What is the best GPU for Llama 3.1 70B Instruct?
- The NVIDIA GeForce RTX 5090 delivers the best performance for Llama 3.1 70B Instruct, achieving 14 tok/s at Q4_K_M with an good rating.
- What quantization should I use for Llama 3.1 70B Instruct?
- For the best quality, use Q5_K_M (47 GB VRAM). If your GPU has limited VRAM, Q2_K (24.5 GB) is the most efficient option with acceptable quality.
- Is Llama 3.1 70B Instruct good for coding?
- Yes. Llama 3.1 70B Instruct is used with Cursor, Open WebUI for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
Data confidence: verified. Last updated: 2026-03-01. Source