Llama 3.3 70B Instruct — Hardware Requirements & GPU Compatibility
ChatFunctions
Meta Llama 3.3 70B Instruct is a 70-billion parameter large language model from Meta, released as part of the Llama 3.3 generation. It is an instruction-tuned model optimized for dialogue and chat use cases, offering strong performance across reasoning, coding, and multilingual tasks. Llama 3.3 70B delivers quality competitive with much larger models while remaining feasible to run on high-end consumer or workstation GPUs with sufficient VRAM. The model uses a grouped-query attention architecture with a 128K token context window and was trained on a massive multilingual corpus. It is released under the Llama 3.3 Community License, making it one of the most capable openly available models for local inference.
Doesn't fit a 24 GB GPU
Smallest consumer fit → Mac Studio M1 Max (32 GB) · IQ2_XS · 28 GB · ~12 tok/s
Macs from 32 GB (IQ2_XS · ~12 tok/s)
Score 80#89 of 106 open#35 of 51 that fit ≤64 GBllama3.3
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
80.0
- 90% range 76.0–84.0
- #89 of 106 open models
- #35 of 51 that fit ≤64 GB
Run Llama 3.3 70B 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 llama3.3:70bQ4_K_M · needs ~51.3 GB · 16K context
Download: Llama-3.3-70B-Instruct-Q4_K_M.gguf42.52 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on A100 80GB (28 GB headroom) · Q4_K_M
- Generation speed
- ~29 tok/s
- generation speed
- Cost per 1M output tokens
- $10.68
- per 1M output tokens
How Much VRAM Does Llama 3.3 70B Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 33.0 GB | 43.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 37.7 GB | |
| 34.0 GB | 44.1 GB | 3-bit small — 3-bit small quantization | Does not fit: needs 38.7 GB | |
| 37.8 GB | 48 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 42.6 GB | |
| 38.8 GB | 48.9 GB | 4-bit · legacy — 4-bit legacy quantization | Does not fit: needs 43.5 GB | |
| 46.6 GB | 56.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 51.3 GB | |
| 55.3 GB | 65.4 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 60 GB | |
| 64.0 GB | 74.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 68.8 GB | |
| 77.6 GB | 87.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 82.3 GB |
Selected Q4_K_M
4-bit medium quantization51.3GBat 16K context
- Weights
- 45.6 GB
- KV cache
- 5.4 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 27.3 GB more
Or .
Which Hardware Can Run Llama 3.3 70B Instruct?
Q4_K_M · 46.6 GBLlama 3.3 70B Instruct (Q4_K_M) requires 46.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 61+ GB is recommended. No consumer GPU has enough memory. 52 devices with unified memory can run Llama 3.3 70B Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
32–40 GB
Q4_K_M · best fit IQ2_M (30.9 GB)
1 GPU · 3 Mac classes · 1 device family
RTX 5090~45Simulate output on RTX 5090M5 Max36 GB~12Simulate output on Mac Studio M5 Max (36 GB)M532 GB~4Simulate output on MacBook Air 13" M5 (32 GB)Orin~5Simulate output on NVIDIA Jetson AGX Orin 32GB64–80 GB
fits · 12.7 GB free · up to 32K ctx
3 Mac classes · 1 device family
M2 Ultra*64 GB~12Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~9Simulate output on Mac Studio M5 Max (64 GB)Orin~3Simulate output on NVIDIA Jetson AGX Orin 64GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Llama 3.3 70B Instruct?
The llmrun Score merges 12 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the ≤64 GB tier
- Llama 3.3 70B Instruct
- Other models
- Best Score for its VRAM
#35 of 51 that fit ≤64 GB
Each dot is a model at the best quant that fits ≤64 GB. 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 ≤64 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 15 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Dec 2024Independent
Independent47.4%
#62/100 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1318.0
#96/188 open
- AIME 2024/2025
Epoch AI · Dec 2024Independent
Independent5.1%
#70/88 open
- MMLU-Pro
TIGER-LabIndependent
Independent65.9%
#44/146 open
- SciCode
Epoch AI · Dec 2024Lab-reported
Lab-reported26.0%
#64/72 open
- SimpleBench
Epoch AI · Dec 2024Independent
Independent19.9%
#23/25 open
In the ≤64 GB class
51 models fit ≤64 GBSame Score, only models that fit ≤64 GB at their best quant: the ranking a ≤64 GB owner actually chooses from.
- #1
Qwen3.8 27BAlibaba
122.0
~56 tok/s · 17.9 GB
- #2
Qwen3.6 27BAlibaba
111.2
~56 tok/s · 17.9 GB
- #3
Qwen3.5 122B A10BAlibaba
109.4
~62 tok/s · 63.0 GB
- #4
Qwen3.6 35B A3BAlibaba
106.8
~91 tok/s · 22.0 GB
- #5
Gemma 4 31B ITGoogle
106.7
~48 tok/s · 21.7 GB
- #6
Gemma 4 26B A4B ITGoogle
105.8
~114 tok/s · 16.5 GB
Alternatives to Llama 3.3 70B 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
Qwen2.5 72B Instruct
Alibaba
- 2.2 higher Score
- 2 GB less VRAM
Score 82.2 · 49.3 GB
Other vendor
Mistral Medium 3.5 128B
Mistral AI
- 22.1 higher Score
- 31.5 GB more VRAM
Score 102.1 · 82.8 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 122B A10BAlibaba | 109.4 | 75.5 GB | —needs > 24 GB | similar score |
| GLM 4.5 AirZ.ai | 98.6 | 67.6 GB | —needs > 24 GB | similar score |
| GPT OSS 120BOpenAI | 97.5 | 70.8 GB | —needs > 24 GB | similar score |
| NVIDIA Nemotron 3 Super 120B A12B BF16NVIDIA | 95.8 | 74.7 GB | —needs > 24 GB | similar score |
| Phi 4Microsoft | 83.9 | 12.5 GB | ~69 | similar score |
| Mistral Large Instruct 2411Mistral AI | 82.5 | 79.8 GB | —needs > 24 GB | similar score |
| Llama 4 Scout 17B 16E InstructMeta | 82.1 | 77 GB | —needs > 24 GB | similar score |
| Mistral Large Instruct 2407Mistral AI | 81.7 | 86.4 GB | —needs > 24 GB | similar score |
| C4ai Command A 03 2025Cohere | 79.8 | 78.7 GB | —needs > 24 GB | similar score |
| Gemma 3 27B ITGoogle | 79.4 | 21.7 GB | ~36 | similar score |
Specifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 70.6B
- Context Length
- 131,072 tokens
- Release Date
- 2024-11-26
- License
- llama3.3
Frequently Asked Questions
How much VRAM does Llama 3.3 70B Instruct need?
Llama 3.3 70B Instruct requires 46.6 GB of VRAM at Q4_K_M, or 155.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB
KV Cache + Overhead ≈ 4.3 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
Can NVIDIA GeForce RTX 4090 run Llama 3.3 70B Instruct?
Yes, at IQ2_XS (23.3 GB) or lower. Higher quantizations like IQ2_S (24.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Llama 3.3 70B Instruct?
For Llama 3.3 70B Instruct, Q4_K_M (46.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (47.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 21.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3.3 70B Instruct on a Mac?
Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Llama 3.3 70B Instruct, the smallest at IQ2_XS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 46.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (64 GB).
Can I run Llama 3.3 70B Instruct locally?
Yes — Llama 3.3 70B Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3.3 70B Instruct?
At Q4_K_M, Llama 3.3 70B Instruct can reach ~103 tok/s on AMD Instinct MI350X. 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 ÷ 46.6 × 0.65 = ~112 tok/s
Estimated speed at Q4_K_M (46.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 3.3 70B Instruct?
At Q4_K_M, the download is about 42.33 GB. The full-precision BF16 version is 141.11 GB. The smallest option (IQ2_XXS) is 19.40 GB.
Which GPUs can run Llama 3.3 70B Instruct?
No single consumer GPU has enough VRAM to run Llama 3.3 70B Instruct at Q4_K_M (46.6 GB). Multi-GPU or professional hardware is required.
Which devices can run Llama 3.3 70B Instruct?
52 devices with unified memory can run Llama 3.3 70B Instruct at Q4_K_M (46.6 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.