falcon mamba 7b instruct Q4 K M needs ~7.6 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~68 tok/s.
Operating mode
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
Fit status
Runs well
Decode
67.9 tok/s
TTFT
2853 ms
Safe context
180K
Memory
7.6 GB / 16.0 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 67.9 tok/s | 1556 ms | 180K |
| Coding | C | Runs well | 67.9 tok/s | 2853 ms | 180K |
| Agentic Coding | C | Runs well | 67.9 tok/s | 4149 ms | 180K |
| Reasoning | C | Runs well | 67.9 tok/s | 3371 ms | 180K |
| RAG | C | Runs well | 67.9 tok/s | 5186 ms | 180K |
How falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C47 |
Q3_K_S | 3 | 3.4 GB | Low | C47 |
NVFP4 | 4 |
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 startYes, Intel Arc A770 16GB can run falcon mamba 7b instruct Q4 K M with a C grade (Runs well). Expected decode speed: 67.9 tok/s.
falcon mamba 7b instruct Q4 K M (7B parameters) requires approximately 7.6 GB of memory with Q4_K_M quantization.
The recommended quantization for falcon mamba 7b instruct Q4 K M is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A770 16GB, falcon mamba 7b instruct Q4 K M achieves approximately 67.9 tokens per second decode speed with a time-to-first-token of 2853ms using Q4_K_M quantization.
For coding workloads, falcon mamba 7b instruct Q4 K M on Intel Arc A770 16GB receives a C grade with 67.9 tok/s and 180K context.
On Intel Arc A770 16GB, falcon mamba 7b instruct Q4 K M can safely use up to 180K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-tiiuae--falcon-mamba-7b-instruct-q4-k-m-gguf-on-arc-a770-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C48 |
Q4_K_M | 4 | 4.3 GB | Medium | C48 |
Q5_K_M | 5 | 5.0 GB | High | C49 |
Q6_K | 6 | 5.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C51 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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.
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.