RX 7900 XT 20GBBudget pick
20 GB VRAM (+4)800 GB/s (+576)
C
Raises estimated decode speed by about 201%.98 tok/s decode
Raises estimated decode speed by about 201%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
falcon mamba 7b instruct Q4 K M needs ~7.6 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~33 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
32.6 tok/s
TTFT
5943 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 | 32.6 tok/s | 3242 ms | 180K |
| Coding | C | Runs well | 32.6 tok/s | 5943 ms | 180K |
| Agentic Coding | C | Runs well | 32.6 tok/s | 8644 ms | 180K |
| Reasoning | C | Runs well | 32.6 tok/s | 7023 ms | 180K |
| RAG | C | Runs well | 32.6 tok/s | 10805 ms | 180K |
How falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on Intel Arc Pro B50 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 startUpgrade options
Raises estimated decode speed by about 201%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Raises estimated decode speed by about 201%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Yes, Intel Arc Pro B50 16GB can run falcon mamba 7b instruct Q4 K M with a C grade (Runs well). Expected decode speed: 32.6 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 Pro B50 16GB, falcon mamba 7b instruct Q4 K M achieves approximately 32.6 tokens per second decode speed with a time-to-first-token of 5943ms using Q4_K_M quantization.
For coding workloads, falcon mamba 7b instruct Q4 K M on Intel Arc Pro B50 16GB receives a C grade with 32.6 tok/s and 180K context.
On Intel Arc Pro B50 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-pro-b50-16gb" 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.