MacBook Pro M3 Pro 18GBBudget pick
18 GB Unified (+2)150 GB/s (+82)
B
Raises estimated decode speed by about 168%.27.6 tok/s decode
Raises estimated decode speed by about 168%.
~$1,999 MSRP
Samantha 7B needs ~8.9 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~10 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
10.3 tok/s
TTFT
18848 ms
Safe context
4K
Memory
8.9 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 | B | Runs well | 10.3 tok/s | 10281 ms | 4K |
| Coding | B | Runs well | 9.6 tok/s | 20262 ms | 4K |
| Agentic Coding | B | Tight fit | 10.3 tok/s | 27415 ms | 4K |
| Reasoning | B | Runs well | 10.3 tok/s | 22275 ms | 4K |
| RAG | B | Tight fit | 10.3 tok/s | 34269 ms | 4K |
How Samantha 7B (7B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B65 |
Q3_K_S | 3 | 3.4 GB | Low | B66 |
NVFP4 | 4 |
Copy-paste commands to run Samantha 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "cognitivecomputations/samantha-1.1-llama-7b" \
--hf-file "samantha-1.1-llama-7b-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Raises estimated decode speed by about 168%.
~$1,999 MSRP
Raises estimated decode speed by about 373%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 467%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Yes, MacBook Air M1 16GB can run Samantha 7B with a B grade (Runs well). Expected decode speed: 9.6 tok/s.
Samantha 7B (7B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.
The recommended quantization for Samantha 7B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Air M1 16GB, Samantha 7B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20262ms using Q4_K_M quantization.
For coding workloads, Samantha 7B on MacBook Air M1 16GB receives a B grade with 9.6 tok/s and 4K context.
On MacBook Air M1 16GB, Samantha 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/samantha-7b-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>
Preview:
3.9 GB |
| Medium |
| B67 |
Q4_K_M | 4 | 4.3 GB | Medium | B68 |
Q5_K_M | 5 | 5.0 GB | High | B69 |
Q6_K | 6 | 5.7 GB | High | B69 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B68 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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.