Mac Studio M2 Ultra 64GBBudget pick
64 GB Unified (+24)
C
This setup is broadly balanced for this model.98 tok/s decode
~$3,999 MSRP
falcon mamba 7b instruct Q4 K M needs ~10.3 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~98 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
98.0 tok/s
TTFT
1976 ms
Safe context
595K
Memory
10.3 GB / 40.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 98.0 tok/s | 1078 ms | 595K |
| Coding | C | Runs well | 98.0 tok/s | 1976 ms | 595K |
| Agentic Coding | C | Runs well | 98.0 tok/s | 2873 ms | 595K |
| Reasoning | C | Runs well | 98.0 tok/s | 2335 ms | 595K |
| RAG | C | Runs well | 98.0 tok/s | 3592 ms | 595K |
How falcon mamba 7b instruct Q4 K M (7B params) fits at each quantization level on NVIDIA A100 40GB (40.0 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 | C42 |
Q4_K_M | 4 | 4.3 GB | Medium | C42 |
Q5_K_M | 5 | 5.0 GB | High | C42 |
Q6_K | 6 | 5.7 GB | High | C43 |
Q8_0 | 8 | 7.5 GB | Very High | C43 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | C45 |
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
Yes, NVIDIA A100 40GB can run falcon mamba 7b instruct Q4 K M with a C grade (Runs well). Expected decode speed: 98.0 tok/s.
falcon mamba 7b instruct Q4 K M (7B parameters) requires approximately 10.3 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 NVIDIA A100 40GB, falcon mamba 7b instruct Q4 K M achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, falcon mamba 7b instruct Q4 K M on NVIDIA A100 40GB receives a C grade with 98.0 tok/s and 595K context.
On NVIDIA A100 40GB, falcon mamba 7b instruct Q4 K M can safely use up to 595K 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-a100-40gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: