Radeon Pro W7900 48GBコスパ重視
864 GB/s (+464)
C
Raises estimated decode speed by about 112%.34.8 tok/sデコード
Raises estimated decode speed by about 112%.
〜$3,999 MSRP
Can cognitivecomputations Dolphin3.0 R1 Mistral 24B run on MacBook Pro M3 Max 64GB?
YES — Runs Great
C48Usable
Estimated from fit model
cognitivecomputations Dolphin3.0 R1 Mistral 24B needs ~25.3 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~16 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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) — 25.3 GB, 16.4 tok/s, Runs well
25.3 GB required46.1 GB available
55% VRAM used
Fit status
Runs well
Decode
16.4 tok/s
TTFT
11810 ms
Safe context
134K
Memory
25.3 GB / 46.1 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom6.9 GB
See how fast it feels
See how fast it feelscognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M3 Max 64GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 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 | C | Runs well | 16.4 tok/s | 6442 ms | 134K |
| Coding | C | Runs well | 16.4 tok/s | 11810 ms | 134K |
| Agentic Coding | C | Runs well | 16.4 tok/s | 17178 ms | 134K |
| Reasoning | C | Runs well | 16.4 tok/s | 13957 ms | 134K |
| RAG | C | Runs well | 16.4 tok/s | 21472 ms | 134K |
Inference speed
cognitivecomputations Dolphin3.0 R1 Mistral 24B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for cognitivecomputations Dolphin3.0 R1 Mistral 24B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~82 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 | 82.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 52.3 | Tight |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 47.2 | Tight |
| RTX 3090 24GB | 24 GB | Q4_K_M | 44.8 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 38.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 34.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 34.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 31.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 30.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.5 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 19.1 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 16.4 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 15.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 6.7 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 4.2 | 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.
Quantization options
How cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C43 |
Q3_K_S | 3 | 11.8 GB | Low | C44 |
NVFP4 | 4 | 13.4 GB | Medium | C44 |
Q4_K_M | 4 | 14.6 GB | Medium | C45 |
Q5_K_M | 5 | 17.3 GB | High | C46 |
Q6_K | 6 | 19.7 GB | High | C46 |
Q8_0Best for your GPU | 8 | 25.7 GB | Very High | C48 |
F16 | 16 | 49.2 GB | Maximum | F0 |
Get started
Copy-paste commands to run cognitivecomputations Dolphin3.0 R1 Mistral 24B on your machine.
Run
lms load hf-bartowski--cognitivecomputations-dolphin3-0-r1-mistral-24b-gguf && lms server startアップグレードオプション
cognitivecomputations Dolphin3.0 R1 Mistral 24Bを快適に動かすハードウェア
Frequently asked questions
Can MacBook Pro M3 Max 64GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B?
Yes, MacBook Pro M3 Max 64GB can run cognitivecomputations Dolphin3.0 R1 Mistral 24B with a C grade (Runs well). Expected decode speed: 16.4 tok/s.
How much VRAM does cognitivecomputations Dolphin3.0 R1 Mistral 24B need?
cognitivecomputations Dolphin3.0 R1 Mistral 24B (24B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.
What is the best quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B?
The recommended quantization for cognitivecomputations Dolphin3.0 R1 Mistral 24B is Q4_K_M, which balances quality and memory efficiency.
What speed will cognitivecomputations Dolphin3.0 R1 Mistral 24B run at on MacBook Pro M3 Max 64GB?
On MacBook Pro M3 Max 64GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11810ms using Q4_K_M quantization.
Can MacBook Pro M3 Max 64GB run cognitivecomputations Dolphin3.0 R1 Mistral 24B for coding?
For coding workloads, cognitivecomputations Dolphin3.0 R1 Mistral 24B on MacBook Pro M3 Max 64GB receives a C grade with 16.4 tok/s and 134K context.
What context window can cognitivecomputations Dolphin3.0 R1 Mistral 24B use on MacBook Pro M3 Max 64GB?
On MacBook Pro M3 Max 64GB, cognitivecomputations Dolphin3.0 R1 Mistral 24B can safely use up to 134K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for cognitivecomputations Dolphin3.0 R1 Mistral 24B?
Not always. MacBook Pro M3 Max 64GB 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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