MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)
C
Este setup está bastante equilibrado para este modelo.94.4 tok/s decodificación
~$2,499 MSRP
Can Neural Chat 7B run on Radeon Pro W7800 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
Neural Chat 7B needs ~10.3 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~80 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) — 10.3 GB, 85.6 tok/s, Runs well
10.3 GB required32.0 GB available
32% VRAM used
Fit status
Runs well
Decode
85.6 tok/s
TTFT
2263 ms
Safe context
8K
Memory
10.3 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsNeural Chat 7B on Radeon Pro W7800 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: 85.6 tok/s decode · 2.3s TTFT (warm) · 214 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 85.6 tok/s | 1234 ms | 8K |
| Coding | C | Runs well | 79.6 tok/s | 2433 ms | 8K |
| Agentic Coding | C | Runs well | 85.6 tok/s | 3291 ms | 8K |
| Reasoning | C | Runs well | 85.6 tok/s | 2674 ms | 8K |
| RAG | C | Runs well | 85.6 tok/s | 4114 ms | 8K |
Quantization options
How Neural Chat 7B (7B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C42 |
Q3_K_S | 3 | 3.4 GB | Low | C42 |
NVFP4 | 4 | 3.9 GB | Medium | C43 |
Q4_K_M | 4 | 4.3 GB | Medium | C43 |
Q5_K_M | 5 | 5.0 GB | High | C43 |
Q6_K | 6 | 5.7 GB | High | C43 |
Q8_0 | 8 | 7.5 GB | Very High | C44 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C47 |
Get started
Copy-paste commands to run Neural Chat 7B on your machine.
Run
ollama run neural-chatOpciones de mejora
Hardware que ejecuta bien Neural Chat 7B
Frequently asked questions
Can Radeon Pro W7800 32GB run Neural Chat 7B?
Yes, Radeon Pro W7800 32GB can run Neural Chat 7B with a C grade (Runs well). Expected decode speed: 79.6 tok/s.
How much VRAM does Neural Chat 7B need?
Neural Chat 7B (7B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Neural Chat 7B?
The recommended quantization for Neural Chat 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will Neural Chat 7B run at on Radeon Pro W7800 32GB?
On Radeon Pro W7800 32GB, Neural Chat 7B achieves approximately 79.6 tokens per second decode speed with a time-to-first-token of 2433ms using Q4_K_M quantization.
Can Radeon Pro W7800 32GB run Neural Chat 7B for coding?
For coding workloads, Neural Chat 7B on Radeon Pro W7800 32GB receives a C grade with 79.6 tok/s and 8K context.
What context window can Neural Chat 7B use on Radeon Pro W7800 32GB?
On Radeon Pro W7800 32GB, Neural Chat 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
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