ShellWhisperer 1.5B — Hardware Requirements & GPU Compatibility
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ShellWhisperer 1.5B is a 1.5B-parameter open language model from fableforge-ai. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 1.28 GB of VRAM — see which GPUs and Macs can run it below.
7.7K downloads
10 likesSpecifications
- Publisher
- fableforge-ai
- Parameters
- 1.5B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-06-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does ShellWhisperer 1.5B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.0 GB | 1.9 GB | 0.66 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 1.1 GB | 2.0 GB | 0.75 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.1 GB | 2.0 GB | 0.77 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.3 GB | 2.2 GB | 0.93 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.5 GB | 2.3 GB | 1.10 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.6 GB | 2.5 GB | 1.27 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.9 GB | 2.8 GB | 1.54 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run ShellWhisperer 1.5B?
Q4_K_M · 1.3 GBShellWhisperer 1.5B (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 33K context window can add up to 0.9 GB, bringing total usage to 2.2 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run ShellWhisperer 1.5B?
Q4_K_M · 1.3 GB59 devices with unified memory can run ShellWhisperer 1.5B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does ShellWhisperer 1.5B need?
ShellWhisperer 1.5B requires 1.3 GB of VRAM at Q4_K_M, or 3.5 GB at FP16. Full 33K context adds up to 0.9 GB (2.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.5B × 4.8 bits ÷ 8 = 0.9 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.3 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M1.3 GBQ4_K_M + full context2.2 GB- What's the best quantization for ShellWhisperer 1.5B?
For ShellWhisperer 1.5B, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.8 GB.
VRAM requirement by quantization
IQ2_XXS0.8 GBQ3_K_M1.1 GBIQ4_XS1.2 GBQ4_K_M ★1.3 GBQ6_K1.6 GBFP163.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run ShellWhisperer 1.5B on a Mac?
ShellWhisperer 1.5B requires at least 0.8 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run ShellWhisperer 1.5B locally?
Yes — ShellWhisperer 1.5B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is ShellWhisperer 1.5B?
At Q4_K_M, ShellWhisperer 1.5B can reach ~3438 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~512 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.3 × 0.65 = ~4063 tok/s
Estimated speed at Q4_K_M (1.3 GB)
~4063 tok/s~512 tok/s~4063 tok/s~3438 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of ShellWhisperer 1.5B?
At Q4_K_M, the download is about 0.93 GB. The full-precision FP16 version is 3.09 GB. The smallest option (IQ2_XXS) is 0.42 GB.
- Which GPUs can run ShellWhisperer 1.5B?
50 consumer GPUs can run ShellWhisperer 1.5B at Q4_K_M (1.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run ShellWhisperer 1.5B?
59 devices with unified memory can run ShellWhisperer 1.5B at Q4_K_M (1.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.