MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)546 GB/s (+34)
C
This setup is broadly balanced for this model.92.6 tok/s Dekodierung
ca. $2,499 MSRP
Can falcon mamba 7b instruct Q4 K M run on Radeon Pro W6800 32GB?
YES — Runs Great
C47Usable
Estimated from fit model
falcon mamba 7b instruct Q4 K M needs ~9.2 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~77 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) — 9.2 GB, 77.2 tok/s, Runs well
9.2 GB required32.0 GB available
29% VRAM used
Fit status
Runs well
Decode
77.2 tok/s
TTFT
2507 ms
Safe context
461K
Memory
9.2 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsfalcon mamba 7b instruct Q4 K M on Radeon Pro W6800 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: 77.2 tok/s decode · 2.5s TTFT (warm) · 193 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 | 77.2 tok/s | 1368 ms | 461K |
| Coding | C | Runs well | 77.2 tok/s | 2507 ms | 461K |
| Agentic Coding | C | Runs well | 77.2 tok/s | 3647 ms | 461K |
| Reasoning | C | Runs well | 77.2 tok/s | 2963 ms | 461K |
| RAG | C | Runs well | 77.2 tok/s | 4558 ms | 461K |
Inference speed
falcon mamba 7b instruct Q4 K M inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for falcon mamba 7b instruct Q4 K M 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 |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 92.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 92.6 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 88.5 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 64.6 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 59.3 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 56.6 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 55.6 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 53.5 | Tight |
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 falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C43 |
Q3_K_S | 3 | 3.4 GB | Low | C43 |
NVFP4 | 4 | 3.9 GB | Medium | C43 |
Q4_K_M | 4 | 4.3 GB | Medium | C43 |
Q5_K_M | 5 | 5.0 GB | High | C44 |
Q6_K | 6 | 5.7 GB | High | C44 |
Q8_0 | 8 | 7.5 GB | Very High | C44 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C48 |
Get started
Copy-paste commands to run falcon mamba 7b instruct Q4 K M on your machine.
Run
lms load hf-tiiuae--falcon-mamba-7b-instruct-q4-k-m-gguf && lms server startUpgrade-Optionen
Hardware, die falcon mamba 7b instruct Q4 K M gut ausführt
Frequently asked questions
Can Radeon Pro W6800 32GB run falcon mamba 7b instruct Q4 K M?
Yes, Radeon Pro W6800 32GB can run falcon mamba 7b instruct Q4 K M with a C grade (Runs well). Expected decode speed: 77.2 tok/s.
How much VRAM does falcon mamba 7b instruct Q4 K M need?
falcon mamba 7b instruct Q4 K M (7B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
What is the best quantization for falcon mamba 7b instruct Q4 K M?
The recommended quantization for falcon mamba 7b instruct Q4 K M is Q4_K_M, which balances quality and memory efficiency.
What speed will falcon mamba 7b instruct Q4 K M run at on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, falcon mamba 7b instruct Q4 K M achieves approximately 77.2 tokens per second decode speed with a time-to-first-token of 2507ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run falcon mamba 7b instruct Q4 K M for coding?
For coding workloads, falcon mamba 7b instruct Q4 K M on Radeon Pro W6800 32GB receives a C grade with 77.2 tok/s and 461K context.
What context window can falcon mamba 7b instruct Q4 K M use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, falcon mamba 7b instruct Q4 K M can safely use up to 461K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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