MacBook Pro M4 Max 36GBGünstige Wahl
36 GB Unified (+12)
A
This setup is broadly balanced for this model.70.9 tok/s Dekodierung
ca. $2,499 MSRP
Can WizardMath 7B run on Intel Arc Pro B60 24GB?
YES — Runs Great
B70Good
Estimated from fit model
WizardMath 7B needs ~9.5 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~62 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 9.5 GB, 62.0 tok/s, Runs well
9.5 GB required24.0 GB available
40% VRAM used
Fit status
Runs well
Decode
62.0 tok/s
TTFT
3123 ms
Safe context
4K
Memory
9.5 GB / 24.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsWizardMath 7B on Intel Arc Pro B60 24GB
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: 62.0 tok/s decode · 3.1s TTFT (warm) · 155 tok/s prefill
What limits this setup
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Best improvement path
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 62.0 tok/s | 1703 ms | 4K |
| Coding | B | Runs well | 62.0 tok/s | 3123 ms | 4K |
| Agentic Coding | A | Runs well | 62.0 tok/s | 4542 ms | 4K |
| Reasoning | B | Runs well | 62.0 tok/s | 3691 ms | 4K |
| RAG | A | Runs well | 62.0 tok/s | 5678 ms | 4K |
Inference speed
WizardMath 7B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for WizardMath 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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 | 98.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 98.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 95.2 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 60.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 59.8 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 55.4 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 48.7 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 46.0 | Offloads |
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 WizardMath 7B (7B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B65 |
Q3_K_S | 3 | 3.4 GB | Low | B65 |
NVFP4 | 4 | 3.9 GB | Medium | B66 |
Q4_K_M | 4 | 4.3 GB | Medium | B66 |
Q5_K_M | 5 | 5.0 GB | High | B66 |
Q6_K | 6 | 5.7 GB | High | B66 |
Q8_0 | 8 | 7.5 GB | Very High | B68 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | A71 |
Get started
Copy-paste commands to run WizardMath 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "WizardLMTeam/WizardMath-7B-V1.1" \
--hf-file "WizardMath-7B-V1.1-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
Hardware, die WizardMath 7B gut ausführt
Frequently asked questions
Can Intel Arc Pro B60 24GB run WizardMath 7B?
Yes, Intel Arc Pro B60 24GB can run WizardMath 7B with a B grade (Runs well). Expected decode speed: 62.0 tok/s.
How much VRAM does WizardMath 7B need?
WizardMath 7B (7B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.
What is the best quantization for WizardMath 7B?
The recommended quantization for WizardMath 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will WizardMath 7B run at on Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, WizardMath 7B achieves approximately 62.0 tokens per second decode speed with a time-to-first-token of 3123ms using Q4_K_M quantization.
Can Intel Arc Pro B60 24GB run WizardMath 7B for coding?
For coding workloads, WizardMath 7B on Intel Arc Pro B60 24GB receives a B grade with 62.0 tok/s and 4K context.
What context window can WizardMath 7B use on Intel Arc Pro B60 24GB?
On Intel Arc Pro B60 24GB, WizardMath 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.
What should I upgrade first if WizardMath 7B feels slow on Intel Arc Pro B60 24GB?
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Would CUDA be a better path than Intel Arc Pro B60 24GB for WizardMath 7B?
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
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