Falcon 40B Instruct needs ~35.0 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q5_K_M quantization, expect ~11 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
3.0 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.5 GB host RAM)
Decode
11.0 tok/s
TTFT
17597 ms
Safe context
4K
Memory
35.0 GB / 32.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 2.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload (needs ~1.8 GB host RAM) | 11.6 tok/s | 9078 ms | 4K |
| Coding | B | Very compromised (needs ~2.5 GB host RAM) | 11.0 tok/s | 17597 ms | 4K |
| Agentic Coding | B | Very compromised (needs ~3.8 GB host RAM) | 9.9 tok/s | 28493 ms | 4K |
| Reasoning | B | Very compromised (needs ~2.5 GB host RAM) | 11.0 tok/s | 20796 ms | 4K |
| RAG | B | Very compromised (needs ~3.8 GB host RAM) | 9.9 tok/s | 35616 ms | 4K |
How Falcon 40B Instruct (40B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 15.6 GB | Low | A70 |
Q3_K_S | 3 | 19.6 GB | Low | B70 |
NVFP4 | 4 | 22.4 GB | Medium | B69 |
Q4_K_MBest for your GPU | 4 | 24.4 GB | Medium | B69 |
Q5_K_M | 5 | 28.8 GB | High | F0 |
Q6_K | 6 | 32.8 GB | High | F0 |
Q8_0 | 8 | 42.8 GB | Very High | F0 |
F16 | 16 | 82.0 GB | Maximum | F0 |
Copy-paste commands to run Falcon 40B Instruct on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "tiiuae/falcon-40b-instruct" \
--hf-file "falcon-40b-instruct-Q5_K_M.gguf" \
-c 4096 -ngl 99升级选项
RTX A6000 48GB经济之选Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 105%.
~$4,650 MSRP
RTX PRO 5000 Blackwell 48GB最佳性价比Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 295%.
~$4,999 MSRP
NVIDIA L40 48GBNVIDIA 升级Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 136%.
~$5,500 MSRP
Yes, RTX 5000 Ada 32GB can run Falcon 40B Instruct with a B grade (Very compromised (needs ~2.5 GB host RAM)). Expected decode speed: 11.0 tok/s.
Falcon 40B Instruct (40B parameters) requires approximately 35.0 GB of memory with Q5_K_M quantization.
The recommended quantization for Falcon 40B Instruct is Q5_K_M, which balances quality and memory efficiency.
On RTX 5000 Ada 32GB, Falcon 40B Instruct achieves approximately 11.0 tokens per second decode speed with a time-to-first-token of 17597ms using Q5_K_M quantization.
For coding workloads, Falcon 40B Instruct on RTX 5000 Ada 32GB receives a B grade with 11.0 tok/s and 4K context.
On RTX 5000 Ada 32GB, Falcon 40B Instruct can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/falcon-40b-instruct-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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