Falcon 40B Instruct needs ~98.1 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~3 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
6.3 tok/s
TTFT
30687 ms
Safe context
8K
Memory
44.9 GB / 108.8 GB
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 6.3 tok/s | 16738 ms | 8K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | B | Runs well | 6.3 tok/s | 44636 ms | 8K |
| Reasoning | B | Runs well | 6.3 tok/s | 36266 ms | 8K |
| RAG | B | Runs well | 6.3 tok/s | 55795 ms | 8K |
How Falcon 40B Instruct (40B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 15.6 GB | Low | B61 |
Q3_K_S | 3 | 19.6 GB | Low | B62 |
NVFP4 | 4 | 22.4 GB | Medium | B62 |
Q4_K_M | 4 | 24.4 GB | Medium | B62 |
Q5_K_M | 5 | 28.8 GB | High | B63 |
Q6_K | 6 | 32.8 GB | High | B64 |
Q8_0Best for your GPU | 8 | 42.8 GB | Very High | B66 |
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アップグレードオプション
NVIDIA H200 141GBコスパ重視Raises estimated decode speed by about 2365%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
NVIDIA H200 PCIe 141GBコスパ最良Raises estimated decode speed by about 2365%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
NVIDIA B200 180GBNVIDIAアップグレードRaises estimated decode speed by about 4008%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run Falcon 40B Instruct at F16 quantization (Tight fit). The recommended Q5_K_M requires 31.8 GB which exceeds available memory, but at F16 it needs only 98.1 GB. Expected decode speed: 3.0 tok/s.
Falcon 40B Instruct (40B parameters) requires approximately 31.8 GB at Q5_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 98.1 GB.
The recommended quantization is Q5_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 98.1 GB.
On NVIDIA DGX Spark 128GB, Falcon 40B Instruct achieves approximately 3.0 tokens per second decode speed with a time-to-first-token of 63657ms using F16 quantization.
For coding workloads, Falcon 40B Instruct on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, Falcon 40B Instruct can safely use up to 8K tokens of context at F16 quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/falcon-40b-instruct-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: