TinyLlama 1.1B needs ~5.4 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~15 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
Runs well
Decode
15.4 tok/s
TTFT
12571 ms
Safe context
4K
Memory
5.4 GB / 23.0 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 | C | Runs well | 15.4 tok/s | 6857 ms | 4K |
| Coding | C | Runs well | 15.4 tok/s | 12571 ms | 4K |
| Agentic Coding | C | Runs well | 15.4 tok/s | 18286 ms | 4K |
| Reasoning | C | Runs well | 15.4 tok/s | 14857 ms | 4K |
| RAG | C | Runs well | 15.4 tok/s | 22857 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for TinyLlama 1.1B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~21 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 | 20.9 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 17.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 17.6 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 15.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 15.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 15.4 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 15.4 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 15.4 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 15.4 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 15.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 15.4 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 15.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 15.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 15.4 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 15.4 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 15.4 | Fits |
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 TinyLlama 1.1B (1.100000023841858B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | B55 |
Q3_K_S | 3 | 0.5 GB | Low | B55 |
NVFP4 | 4 | 0.6 GB | Medium | B55 |
Q4_K_M | 4 | 0.7 GB | Medium | B55 |
Q5_K_M | 5 | 0.8 GB | High | B55 |
Q6_K | 6 | 0.9 GB | High | B55 |
Q8_0 | 8 | 1.2 GB | Very High | B56 |
F16Best for your GPU | 16 | 2.3 GB | Maximum | B56 |
Copy-paste commands to run TinyLlama 1.1B on your machine.
Run
ollama run tinyllamaYes, Mac mini M4 32GB can run TinyLlama 1.1B with a C grade (Runs well). Expected decode speed: 15.4 tok/s.
TinyLlama 1.1B (1.100000023841858B parameters) requires approximately 5.4 GB of memory with Q4_K_M quantization.
The recommended quantization for TinyLlama 1.1B is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M4 32GB, TinyLlama 1.1B achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.
For coding workloads, TinyLlama 1.1B on Mac mini M4 32GB receives a C grade with 15.4 tok/s and 4K context.
On Mac mini M4 32GB, TinyLlama 1.1B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Not always. Mac mini M4 32GB 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/tinyllama-1.1b-on-m4-mini-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: