ChatCodingAI CodingReasoningMulti-Purpose8.03B
Llama 3.1 8B Instruct
Llama · Llama 3.1 Community License
Best-in-class 8B model. Strong general capabilities with excellent coding support. The go-to small model for local inference — fast, accurate, and well-supported across all inference engines.
- Parameters
- 8.03B
- Context
- 131,072 tokens
- Released
- 2024-07-23
- Engines
- llama.cpp, ollama, vLLM, TGI
- Builder Tools
- Cursor, Continue, Aider, Open WebUI, LM Studio
Llama 3.1 8B Instruct (8.03B) requires 6.7 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 4.9 GB VRAM, making it compatible with the NVIDIA GeForce RTX 3060 12GB. On NVIDIA GeForce RTX 5090, expect approximately 170 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
— OwnRig methodology, data updated 2026-03-01
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 8.9 GB | 8 GB |
| recommended | Q6_K | 6.7 GB | 5.8 GB |
| recommended | Q5_K_M | 5.8 GB | 4.8 GB |
| efficient | Q4_K_M | 4.9 GB | 4 GB |
| compressed | Q3_K_M | 4 GB | 3.1 GB |
Context Length Impact
KV cache VRAM at Q6_K quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 102 MB | 6.8 GB |
| 4K | 307 MB | 7 GB |
| 8K | 512 MB | 7.2 GB |
| 16K | 1 GB | 7.7 GB |
| 32K | 2 GB | 8.7 GB |
| 64K | 4.1 GB | 10.8 GB |
| 128K | 8.2 GB | 14.9 GB |
Compatible GPUs
Performance data for Llama 3.1 8B Instruct across different hardware.
| Device | Quantization | Speed | Rating | Fits in VRAM |
|---|---|---|---|---|
| NVIDIA GeForce RTX 3060 12GB | Q5_K_M | 35 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 4060 Ti 16GB | Q8_0 | 55 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4070 Ti Super | Q8_0 | 75 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4090 | Q8_0 | 95 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 3090 | Q8_0 | 70 tok/s | Excellent | ✓ |
| Apple M4 Pro (24GB Unified) | Q8_0 | 32 tok/s | Good | ✓ |
| Apple M4 Max (36GB Unified) | Q8_0 | 55 tok/s | Excellent | ✓ |
| Apple M4 Max (64GB Unified) | Q8_0 | 55 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 5090 | Q8_0 | 170 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 5080 | Q8_0 | 92 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4070 Super | Q5_K_M | 55 tok/s | Excellent | ✓ |
| Apple M4 Pro (48GB) | Q8_0 | 32 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 4080 Super | Q8_0 | 82 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4060 8GB | Q4_K_M | 32 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q5_K_M | 52 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 3080 10GB | Q5_K_M | 50 tok/s | Excellent | ✓ |
| Apple M3 Pro (18GB Unified) | Q4_K_M | 15 tok/s | Acceptable | ✓ |
Builder Context
Llama 3.1 8B Instruct is commonly used with Cursor, Continue, Aider, Open WebUI, LM Studio. 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 8B Instruct.
Mid-Range
High-EndBudget
Budget
Mid-Range
High-EndHigh-End
High-End
Mid-RangeMid-RangeExtreme
Mid-RangeBudget
Mid-RangeFull AI Builder
AI Builder Workstation
NVIDIA GeForce RTX 4090 · 64GB DDR5-5600 (2x32GB)
$2,90224 GB VRAM
Runs 10 models
High-EndFull AI Builder
AMD AI Powerhouse
AMD Radeon RX 7900 XTX 24GB · 32GB DDR5-5600 (2x16GB)
$1,81824 GB VRAM
Runs 7 models
BudgetBasic AI Coding
Budget AI Desktop
NVIDIA GeForce RTX 3060 12GB · 32GB DDR4-3200 (2x16GB)
$75312 GB VRAM
Runs 7 models
BudgetHome AI Server
Budget Home AI Server
NVIDIA GeForce RTX 4060 Ti 16GB · 32GB DDR5-5200 (2x16GB)
$1,16216 GB VRAM
Runs 7 models
Mid-RangeBasic AI Coding
Compact SFF AI Build
NVIDIA GeForce RTX 4070 Super 12GB · 32GB DDR5-5600 (2x16GB)
$1,47312 GB VRAM
Runs 5 models
High-End
High-End AI Workstation
NVIDIA GeForce RTX 4090 · 64GB DDR5-6000 (2x32GB)
$3,67224 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
Mid-RangePower AI Coding
Mid-Range AI Workstation
NVIDIA GeForce RTX 4060 Ti 16GB · 32GB DDR5-5600 (2x16GB)
$1,22816 GB VRAM
Runs 8 models
Mid-RangeHome AI Server
Mid-Range Home AI Server
NVIDIA GeForce RTX 3090 24GB (Used) · 64GB DDR5-5600 (2x32GB)
$1,89224 GB VRAM
Runs 9 models
ExtremeFull AI Builder
Next-Gen AI Workstation
NVIDIA GeForce RTX 5090 32GB · 64GB DDR5-6000 (2x32GB)
$4,03232 GB VRAM
Runs 6 models
Mid-RangeHome AI Server
Silent Mini-ITX AI Box
NVIDIA GeForce RTX 4060 Ti 16GB · 32GB DDR5-5600 (2x16GB)
$1,25316 GB VRAM
Runs 8 models
Budget
Starter AI Desktop
NVIDIA GeForce RTX 3060 12GB · 16GB DDR4-3200 (2x8GB)
$58212 GB VRAM
Runs 6 models
Frequently Asked Questions
- How much VRAM does Llama 3.1 8B Instruct need?
- Llama 3.1 8B Instruct requires 6.7 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 4 GB.
- What is the best GPU for Llama 3.1 8B Instruct?
- The NVIDIA GeForce RTX 5090 delivers the best performance for Llama 3.1 8B Instruct, achieving 170 tok/s at Q8_0 with an excellent rating.
- Can I run Llama 3.1 8B Instruct on an RTX 4060 Ti?
- Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Llama 3.1 8B Instruct runs at 55 tok/s (Q8_0, excellent).
- What quantization should I use for Llama 3.1 8B Instruct?
- For the best quality, use Q6_K (6.7 GB VRAM). If your GPU has limited VRAM, Q3_K_M (4 GB) is the most efficient option with acceptable quality.
- Is Llama 3.1 8B Instruct good for coding?
- Yes. Llama 3.1 8B Instruct is used with Cursor, Continue, Aider, Open WebUI, LM Studio 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