Steelman 14B Ada — Hardware Requirements & GPU Compatibility
ChatCode
Steelman 14B Ada is a 14B-parameter open language model from the-clanker-lover. At Q4_K_M it needs about 9.24 GB of VRAM — see which GPUs and Macs can run it below.
524 downloads
4 likesSpecifications
- Publisher
- the-clanker-lover
- Parameters
- 14B
- Release Date
- 2026-03-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Steelman 14B Ada Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 9.2 GB | — | 8.40 GB | 4-bit medium quantization — most popular sweet spot |
Which GPUs Can Run Steelman 14B Ada?
Q4_K_M · 9.2 GBSteelman 14B Ada (Q4_K_M) requires 9.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Steelman 14B Ada?
Q4_K_M · 9.2 GB27 devices with unified memory can run Steelman 14B Ada, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Steelman 14B Ada need?
Steelman 14B Ada requires 9.2 GB of VRAM at Q4_K_M.
VRAM = Weights + KV Cache + Overhead
Weights = 14B × 4.8 bits ÷ 8 = 8.4 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M9.2 GB- Can I run Steelman 14B Ada on a Mac?
Steelman 14B Ada requires at least 9.2 GB at Q4_K_M, 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 Steelman 14B Ada locally?
Yes — Steelman 14B Ada can run locally on consumer hardware. At Q4_K_M quantization it needs 9.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Steelman 14B Ada?
At Q4_K_M, Steelman 14B Ada can reach ~316 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~71 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 9.2 × 0.55 = ~316 tok/s
Estimated speed at Q4_K_M (9.2 GB)
~316 tok/s~71 tok/s~236 tok/s~195 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Steelman 14B Ada?
At Q4_K_M, the download is about 8.40 GB.
- Which GPUs can run Steelman 14B Ada?
28 consumer GPUs can run Steelman 14B Ada at Q4_K_M (9.2 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Steelman 14B Ada?
27 devices with unified memory can run Steelman 14B Ada at Q4_K_M (9.2 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.