Falcon 40B Instruct needs ~20.6 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q2_K quantization, expect ~12 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
13.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.7 tok/s
TTFT
72169 ms
Safe context
4K
Memory
33.8 GB / 20.0 GB
Offload
40%
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.8 tok/s | 37156 ms | 4K |
| Coding | F | Too heavy | 2.7 tok/s | 72169 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 117291 ms | 4K |
| Reasoning | F | Too heavy | 2.7 tok/s | 85291 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 146614 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Falcon 40B Instruct at Q5_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~45 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? |
|---|---|---|---|---|
2× RX 7900 XTX 24GB | 48 GB | Q5_K_M | 45.3 | Fits |
 2× RTX 4090 24GB | 48 GB | Q5_K_M | 41.3 | Fits |
| 2× RTX 3090 24GB | 48 GB | Q5_K_M | 35.3 | Fits |
| 4× RTX 3060 12GB | 48 GB | Q5_K_M | 31.1 | Fits |
| RTX 5090 32GB | 32 GB | Q5_K_M | 29.4 | Heavy offload |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 23.1 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 23.1 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 21.5 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 17.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 16.9 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 12.9 | Offloads |
| RTX 4090 24GB | 24 GB | Q5_K_M | 10.5 | Too big |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 9.5 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 9.2 | Tight |
| RTX 3090 24GB | 24 GB | Q5_K_M | 9.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 8.5 | Tight |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 3.7 | Too big |
| RTX 4070 12GB | 12 GB | Q5_K_M | 2.2 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q5_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.
How Falcon 40B Instruct (40B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 15.6 GB | Low | F0 |
Q3_K_S | 3 | 19.6 GB | Low | F0 |
NVFP4 | 4 | 22.4 GB | Medium | F0 |
Q4_K_M | 4 | 24.4 GB | Medium | F0 |
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 5090 32GB最佳性价比Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 989%.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBNVIDIA 升级Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 581%.
~$2,499 MSRP
RTX A6000 48GB经济之选Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$4,650 MSRP
Yes, RTX 4000 Ada 20GB can run Falcon 40B Instruct at Q2_K quantization (Runs with offload (needs ~0.5 GB host RAM)). The recommended Q5_K_M requires 33.8 GB which exceeds available memory, but at Q2_K it needs only 20.6 GB. Expected decode speed: 11.7 tok/s.
Falcon 40B Instruct (40B parameters) requires approximately 33.8 GB at Q5_K_M quantization. On RTX 4000 Ada 20GB, it fits at Q2_K using 20.6 GB.
The recommended quantization is Q5_K_M, but on RTX 4000 Ada 20GB the best fitting quantization is Q2_K, which uses 20.6 GB.
On RTX 4000 Ada 20GB, Falcon 40B Instruct achieves approximately 11.7 tokens per second decode speed with a time-to-first-token of 16562ms using Q2_K quantization.
For coding workloads, Falcon 40B Instruct on RTX 4000 Ada 20GB receives a F grade with 2.7 tok/s and 4K context.
On RTX 4000 Ada 20GB, Falcon 40B Instruct can safely use up to 8K tokens of context at Q2_K quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/falcon-40b-instruct-on-rtx-4000-ada-20gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: