Mac Studio M3 Ultra 256GBコスパ重視
256 GB Unified (+128)819 GB/s (+546)
C
Adds memory headroom for longer context windows and future model growth.21 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$6,999 MSRP
Can Falcon H1 1.5B Instruct run on NVIDIA DGX Spark 128GB?
YES — With F16
C40Usable
Estimated from fit model
Falcon H1 1.5B Instruct needs ~17.5 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~21 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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.
Falcon H1 1.5B Instruct at Q4_K_M needs 2.3 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (17.5 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 15.3 GB, 21.0 tok/s, Runs well
15.3 GB required108.8 GB available
14% VRAM used
Fit status
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
8.5M
Memory
15.3 GB / 108.8 GB
Memory breakdown
Weights0.9 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsFalcon H1 1.5B Instruct on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Runs well | 21.0 tok/s | 5029 ms | 7.5M |
| Coding | F | Too heavy | 21.0 tok/s | 9219 ms | 4K |
| Agentic Coding | D | Runs well | 21.0 tok/s | 13410 ms | 8.5M |
| Reasoning | D | Runs well | 21.0 tok/s | 10895 ms | 8.5M |
| RAG | D | Runs well | 21.0 tok/s | 16762 ms | 8.5M |
Quantization options
How Falcon H1 1.5B Instruct (1.5B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | D39 |
Q3_K_S | 3 | 0.7 GB | Low | D39 |
NVFP4 | 4 | 0.8 GB | Medium | D39 |
Q4_K_M | 4 | 0.9 GB | Medium | D39 |
Q5_K_M | 5 | 1.1 GB | High | D39 |
Q6_K | 6 | 1.2 GB | High | D39 |
Q8_0 | 8 | 1.6 GB | Very High | D39 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | D39 |
Get started
Copy-paste commands to run Falcon H1 1.5B Instruct on your machine.
Run
lms load hf-unsloth--falcon-h1-1-5b-instruct-gguf && lms server startアップグレードオプション
Falcon H1 1.5B Instructを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA DGX Spark 128GB run Falcon H1 1.5B Instruct?
Yes, NVIDIA DGX Spark 128GB can run Falcon H1 1.5B Instruct at F16 quantization (Runs well). The recommended Q4_K_M requires 2.3 GB which exceeds available memory, but at F16 it needs only 17.5 GB. Expected decode speed: 21.0 tok/s.
How much VRAM does Falcon H1 1.5B Instruct need?
Falcon H1 1.5B Instruct (1.5B parameters) requires approximately 2.3 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 17.5 GB.
What is the best quantization for Falcon H1 1.5B Instruct?
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 17.5 GB.
What speed will Falcon H1 1.5B Instruct run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Falcon H1 1.5B Instruct achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using F16 quantization.
Can NVIDIA DGX Spark 128GB run Falcon H1 1.5B Instruct for coding?
For coding workloads, Falcon H1 1.5B Instruct on NVIDIA DGX Spark 128GB receives a F grade with 21.0 tok/s and 4K context.
What context window can Falcon H1 1.5B Instruct use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Falcon H1 1.5B Instruct can safely use up to 8.3M tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Falcon H1 1.5B Instruct?
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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--falcon-h1-1-5b-instruct-gguf-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: