~$1,099 MSRP
Falcon H1 1.5B Instruct needs ~3.6 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 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
24.0 tok/s
TTFT
8067 ms
Safe context
1.1M
Memory
3.6 GB / 16.0 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 | 24.0 tok/s | 4400 ms | 1.0M |
| Coding | C | Runs well | 24.0 tok/s | 8067 ms | 1.1M |
| Agentic Coding | C | Runs well | 24.0 tok/s | 11733 ms | 1.1M |
| Reasoning | C | Runs well | 24.0 tok/s | 9533 ms | 1.1M |
| RAG | C | Runs well | 24.0 tok/s | 14667 ms | 1.1M |
Inference speed
Estimated decode speed (tokens/sec) for Falcon H1 1.5B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~29 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? |
|---|---|---|---|---|
| 32 GB | Q4_K_M | 28.5 | Fits | |
| 24 GB | Q4_K_M | 24.0 | Fits | |
| 16 GB | Q4_K_M | 24.0 | Fits | |
| 24 GB | Q4_K_M | 21.0 | Fits | |
| 12 GB | Q4_K_M | 21.0 | Fits | |
| 12 GB | Q4_K_M | 21.0 | Fits | |
| 8 GB | Q4_K_M | 21.0 | Fits | |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 21.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 21.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 21.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 21.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.0 | Fits |
Estimates for single-stream decoding at Q4_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 H1 1.5B Instruct (1.5B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | C45 |
Q3_K_S | 3 | 0.7 GB | Low | C45 |
NVFP4 | 4 | 0.8 GB | Medium | C45 |
Q4_K_M | 4 | 0.9 GB | Medium | C45 |
Q5_K_M | 5 | 1.1 GB | High | C45 |
Q6_K | 6 | 1.2 GB | High | C45 |
Q8_0 | 8 | 1.6 GB | Very High | C45 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | C47 |
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 startUpgrade options
Yes, RTX 4070 Ti Super 16GB can run Falcon H1 1.5B Instruct with a C grade (Runs well). Expected decode speed: 24.0 tok/s.
Falcon H1 1.5B Instruct (1.5B parameters) requires approximately 3.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Falcon H1 1.5B Instruct is Q4_K_M, which balances quality and memory efficiency.
On RTX 4070 Ti Super 16GB, Falcon H1 1.5B Instruct achieves approximately 24.0 tokens per second decode speed with a time-to-first-token of 8067ms using Q4_K_M quantization.
For coding workloads, Falcon H1 1.5B Instruct on RTX 4070 Ti Super 16GB receives a C grade with 24.0 tok/s and 1.1M context.
On RTX 4070 Ti Super 16GB, Falcon H1 1.5B Instruct can safely use up to 1.1M 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-unsloth--falcon-h1-1-5b-instruct-gguf-on-rtx-4070-ti-super-16gb" 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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