Llama 3.1 8B needs ~9.5 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~9 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
9.0 tok/s
TTFT
21541 ms
Safe context
33K
Memory
9.5 GB / 11.5 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 9.0 tok/s | 11749 ms | 33K |
| Coding | B | Tight fit | 9.0 tok/s | 21541 ms | 33K |
| Agentic Coding | B | Runs with offload | 9.0 tok/s | 31332 ms | 33K |
| Reasoning | B | Tight fit | 9.0 tok/s | 25457 ms | 33K |
| RAG | B | Runs with offload | 9.0 tok/s | 39165 ms | 33K |
Inference speed
Estimated decode speed (tokens/sec) for Llama 3.1 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 102.2 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 96.9 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 83.3 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 82.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 82.6 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 52.9 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 52.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 48.5 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 42.6 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 26.6 | Heavy offload |
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 3.1 8B (8B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A71 |
Q3_K_S | 3 | 3.9 GB | Low | A72 |
NVFP4 | 4 | 4.5 GB | Medium | A73 |
Q4_K_M | 4 | 4.9 GB | Medium | A73 |
Q5_K_M | 5 | 5.8 GB | High | A73 |
Q6_KBest for your GPU | 6 | 6.6 GB | High | A73 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Copy-paste commands to run Llama 3.1 8B on your machine.
Run
ollama run llama3.1Upgrade options
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Raises estimated decode speed by about 67%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
RTX 3080 Ti 12GBBiggest leapRaises estimated decode speed by about 967%.
~$1,199 MSRP
Yes, MacBook Air M1 16GB can run Llama 3.1 8B with a B grade (Tight fit). Expected decode speed: 9.0 tok/s.
Llama 3.1 8B (8B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Llama 3.1 8B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M1 16GB, Llama 3.1 8B achieves approximately 9.0 tokens per second decode speed with a time-to-first-token of 21541ms using Q4_K_M quantization.
For coding workloads, Llama 3.1 8B on MacBook Air M1 16GB receives a B grade with 9.0 tok/s and 33K context.
On MacBook Air M1 16GB, Llama 3.1 8B can safely use up to 33K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. MacBook Air M1 16GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/llama-3.1-8b-on-m1-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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