MacBook Pro M3 Pro 36GBGünstige Wahl
36 GB Unified (+4)150 GB/s (+30)
B
Raises estimated decode speed by about 38%.27.6 tok/s Dekodierung
Raises estimated decode speed by about 38%.
ca. $1,999 MSRP
Can Samantha 7B run on MacBook Pro M4 32GB?
YES — Runs Great
B63Good
Estimated — low-sample bucket· few comparable runs
Samantha 7B needs ~10.6 GB VRAM. MacBook Pro M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~20 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 10.6 GB, 20.0 tok/s, Runs well
10.6 GB required23.0 GB available
46% VRAM used
Fit status
Runs well
Decode
20.0 tok/s
TTFT
9674 ms
Safe context
4K
Memory
10.6 GB / 23.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsSamantha 7B on MacBook Pro M4 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 20.0 tok/s decode · 9.7s TTFT (warm) · 50 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 20.2 tok/s | 5219 ms | 4K |
| Coding | B | Runs well | 20.0 tok/s | 9674 ms | 4K |
| Agentic Coding | B | Runs well | 20.0 tok/s | 14072 ms | 4K |
| Reasoning | B | Runs well | 20.0 tok/s | 11433 ms | 4K |
| RAG | B | Runs well | 20.0 tok/s | 17590 ms | 4K |
Quantization options
How Samantha 7B (7B params) fits at each quantization level on MacBook Pro M4 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B61 |
Q3_K_S | 3 | 3.4 GB | Low | B61 |
NVFP4 | 4 | 3.9 GB | Medium | B61 |
Q4_K_M | 4 | 4.3 GB | Medium | B61 |
Q5_K_M | 5 | 5.0 GB | High | B62 |
Q6_K | 6 | 5.7 GB | High | B62 |
Q8_0 | 8 | 7.5 GB | Very High | B63 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B66 |
Get started
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-Optionen
Hardware, die Samantha 7B gut ausführt
MacBook Pro M4 Max 36GBBestes Preis-Leistungs-Verhältnis
36 GB Unified (+4)410 GB/s (+290)
B
Raises estimated decode speed by about 255%.70.9 tok/s Dekodierung
Raises estimated decode speed by about 255%.
ca. $2,499 MSRP
MacBook Pro M4 Max 48GBApple-Upgrade
48 GB Unified (+16)546 GB/s (+426)
B
Raises estimated decode speed by about 372%.94.4 tok/s Dekodierung
Raises estimated decode speed by about 372%.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Frequently asked questions
Can MacBook Pro M4 32GB run Samantha 7B?
Yes, MacBook Pro M4 32GB can run Samantha 7B with a B grade (Runs well). Expected decode speed: 20.0 tok/s.
How much VRAM does Samantha 7B need?
Samantha 7B (7B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Samantha 7B?
The recommended quantization for Samantha 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Samantha 7B run at on MacBook Pro M4 32GB?
On MacBook Pro M4 32GB, Samantha 7B achieves approximately 20.0 tokens per second decode speed with a time-to-first-token of 9674ms using Q4_K_M quantization.
Can MacBook Pro M4 32GB run Samantha 7B for coding?
For coding workloads, Samantha 7B on MacBook Pro M4 32GB receives a B grade with 20.0 tok/s and 4K context.
What context window can Samantha 7B use on MacBook Pro M4 32GB?
On MacBook Pro M4 32GB, 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.
Is unified memory on MacBook Pro M4 32GB as fast as VRAM for Samantha 7B?
Not always. MacBook Pro 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.
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