Llama 3.3 70B Instruct needs ~58.5 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~11 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Tight fit
Decode
11.0 tok/s
TTFT
17664 ms
Safe context
27K
Memory
58.5 GB / 64.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 11.0 tok/s | 9635 ms | 27K |
| Coding | C | Tight fit | 11.0 tok/s | 17664 ms | 27K |
| Agentic Coding | C | Runs with offload (needs ~1.7 GB host RAM) | 7.5 tok/s | 37378 ms | 27K |
| Reasoning | C | Tight fit | 11.0 tok/s | 20876 ms | 27K |
| RAG | C | Runs with offload (needs ~1.7 GB host RAM) | 7.5 tok/s | 46722 ms | 27K |
Inference speed
Estimated decode speed (tokens/sec) for Llama 3.3 70B Instruct at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 14.6 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 14.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 13.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 10.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 10.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 9.9 | Too big |
 2× RTX 4090 24GB | 48 GB | Q4_K_M | 7.7 | Heavy offload |
| RTX 5090 32GB | 32 GB | Q4_K_M | 7.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 7.0 | Heavy offload |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 6.2 | Heavy offload |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 4.7 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 4.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 3.6 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.7 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.4 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.3 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.1 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Llama 3.3 70B Instruct (70B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C46 |
Q3_K_S | 3 | 34.3 GB | Low | C48 |
NVFP4 | 4 | 39.2 GB | Medium | C48 |
Q4_K_M | 4 | 42.7 GB | Medium | C48 |
Q5_K_MBest for your GPU | 5 | 50.4 GB | High | C48 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
Copy-paste commands to run Llama 3.3 70B Instruct on your machine.
Run
lms load hf-maziyarpanahi--llama-3-3-70b-instruct-gguf && lms server startUpgrade options
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pickRaises estimated decode speed by about 221%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBest valueRaises estimated decode speed by about 185%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
NVIDIA H20 96GBNVIDIA upgradeRaises estimated decode speed by about 590%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP
Yes, NVIDIA A16 64GB can run Llama 3.3 70B Instruct with a C grade (Tight fit). Expected decode speed: 11.0 tok/s.
Llama 3.3 70B Instruct (70B parameters) requires approximately 58.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A16 64GB, Llama 3.3 70B Instruct achieves approximately 11.0 tokens per second decode speed with a time-to-first-token of 17664ms using Q4_K_M quantization.
For coding workloads, Llama 3.3 70B Instruct on NVIDIA A16 64GB receives a C grade with 11.0 tok/s and 27K context.
On NVIDIA A16 64GB, Llama 3.3 70B Instruct can safely use up to 27K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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