Meta Llama 3 8B Instruct — Hardware Requirements & GPU Compatibility
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Meta Llama 3 8B Instruct is the instruction-tuned version of Meta's Llama 3 8B base model, with 8 billion parameters. It is fine-tuned for dialogue and chat use cases using supervised fine-tuning and RLHF, making it ready for conversational applications out of the box. The model supports an 8K token context window and performs well across coding, reasoning, and general knowledge tasks. Its efficient size makes it one of the most popular models for local inference on consumer hardware. Released under the Meta Llama 3 Community License.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~124 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~26 tok/s)
Score 62#105 of 106 open#36 of 36 that fit 24 GBLlama 3 Community
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
62.0
- 90% range 54.1–69.9
- #105 of 106 open models
- #36 of 36 that fit 24 GB
Run Meta Llama 3 8B Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/MaziyarPanahi/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 16.1 GB free · 16K context
Download: Meta-Llama-3-8B-Instruct.Q4_K_M.gguf4.92 GBMaziyarPanahi
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~112 tok/s
- generation speed
- Cost per 1M output tokens
- $0.30
- per 1M output tokens
How Much VRAM Does Meta Llama 3 8B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 3.8 GB | 9.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~175 tok/s | |
| 3.9 GB | 9.4 GB | 3-bit small — 3-bit small quantization | Fits: 32K context · ~170 tok/s | |
| 4.3 GB | 9.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~152 tok/s | |
| 4.4 GB | 9.9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 32K context · ~148 tok/s | |
| 5.3 GB | 10.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~124 tok/s | |
| 6.3 GB | 11.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~104 tok/s | |
| 7.3 GB | 12.8 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~90 tok/s | |
| 8.8 GB | 14.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~74 tok/s |
Selected Q4_K_M
4-bit medium quantization7.9GBat 16K context
- Weights
- 4.6 GB
- KV cache
- 2.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.1 GB free · ~124 tok/s
≥50 tok/s needs ≥407 GB/s · RTX 5090 ~220, RTX 3090 Ti ~124
Which Hardware Can Run Meta Llama 3 8B Instruct?
Q4_K_M · 5.3 GBMeta Llama 3 8B Instruct (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 151 devices with unified memory can run Meta Llama 3 8B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 0.1 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~93Simulate output on RTX 3080RTX 2080 Ti~76Simulate output on RTX 2080 TiRTX 3070 Ti~75Simulate output on RTX 3070 TiOrin Nano~13Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.1 GB free · up to 32K ctx
1 GPU
RX 7900 XT~91Simulate output on RX 7900 XT32–40 GB
fits · 24.1 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~220Simulate output on RTX 5090M5 Max36 GB~61Simulate output on Mac Studio M5 Max (36 GB)M532 GB~20Simulate output on MacBook Air 13" M5 (32 GB)Orin~25Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~17Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Meta Llama 3 8B Instruct?
The llmrun Score merges 5 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the 24 GB tier
- Meta Llama 3 8B Instruct
- Other models
- Best Score for its VRAM
#36 of 36 that fit 24 GB
Each dot is a model at the best quant that fits a 24 GB GPU. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full 24 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 12 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Apr 2024Independent
Independent26.1%
#91/100 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1223.7
#138/188 open
- AIME 2024/2025
Epoch AI · Apr 2024Independent
Independent1.9%
#78/88 open
- MMLU-Pro
TIGER-LabIndependent
Independent41.0%
#100/146 open
- MATH Level 5
Epoch AI · Apr 2024Independent
Independent6.1%
#36/39 open
- MMLU
Epoch AI · Apr 2024Lab-reported
Lab-reported68.8%
#30/85 open
In the 24 GB class
36 models fit 24 GBSame Score, only models that fit 24 GB at their best quant: the ranking a 24 GB owner actually chooses from.
- #1
Qwen3.8 27BAlibaba
122.0
~38 tok/s · 17.9 GB
- #2
Qwen3.6 27BAlibaba
111.2
~38 tok/s · 17.9 GB
- #3
Qwen3.6 35B A3BAlibaba
106.8
~151 tok/s · 22.0 GB
- #4
Gemma 4 31B ITGoogle
106.7
~32 tok/s · 21.7 GB
- #5
Gemma 4 26B A4B ITGoogle
105.8
~171 tok/s · 16.5 GB
- #6
Qwen3.5 35B A3BAlibaba
105.8
~151 tok/s · 22.0 GB
Alternatives to Meta Llama 3 8B Instruct
Llama 3 family · 65 models →Official models with an llmrun Score, each answering one question; numbers are at the best quant that fits a 24 GB GPU.
Best alternative at this size
Qwen3.5 9B
Alibaba
- 37.4 higher Score
- 1.2 GB less VRAM
- 21 tok/s slower
Score 99.4 · 6.6 GB · ~103 tok/s
Much faster, similar quality
Qwen3.5 4B
Alibaba
- 36.5 higher Score
- 4.4 GB less VRAM
- 77 tok/s faster
Score 98.5 · 3.4 GB · ~201 tok/s
Other vendor
GPT OSS 20B
OpenAI
- 34.4 higher Score
- 5.3 GB more VRAM
- 66 tok/s faster
Score 96.4 · 13.1 GB · ~190 tok/s
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3 14BAlibaba | 98.0 | 11.9 GB | ~69 | similar score |
| Qwen3 8BAlibaba | 93.9 | 7.6 GB | ~119 | similar score |
| Phi 4Microsoft | 83.9 | 12.5 GB | ~69 | similar score |
| Gemma 3 12B ITGoogle | 74.2 | 10.9 GB | ~82 | similar score |
| Gemma 2 9B ITGoogle | 68.0 | 7.3 GB | ~107 | similar score |
| Mistral Nemo Instruct 2407Mistral AI | 65.4 | 11 GB | ~81 | similar score |
| Qwen2.5 7B InstructAlibaba | 65.1 | 5.8 GB | ~131 | similar score |
| Gemma 3 4B ITGoogle | 64.0 | 5 GB | ~231 | similar score |
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
Specifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 8.0B
- Release Date
- 2024-04-17
- License
- Llama 3 Community
Frequently Asked Questions
How much VRAM does Meta Llama 3 8B Instruct need?
Meta Llama 3 8B Instruct requires 5.3 GB of VRAM at Q4_K_M, or 17.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Meta Llama 3 8B Instruct?
For Meta Llama 3 8B Instruct, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Meta Llama 3 8B Instruct on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Meta Llama 3 8B Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Meta Llama 3 8B Instruct locally?
Yes — Meta Llama 3 8B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Meta Llama 3 8B Instruct?
At Q4_K_M, Meta Llama 3 8B Instruct can reach ~906 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~124 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.3 × 0.65 = ~981 tok/s
Estimated speed at Q4_K_M (5.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Meta Llama 3 8B Instruct?
At Q4_K_M, the download is about 4.82 GB. The full-precision BF16 version is 16.06 GB. The smallest option (IQ2_XXS) is 2.21 GB.
Which GPUs can run Meta Llama 3 8B Instruct?
52 consumer GPUs can run Meta Llama 3 8B Instruct at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Meta Llama 3 8B Instruct?
151 devices with unified memory can run Meta Llama 3 8B Instruct at Q4_K_M (5.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.