PowerLM 3B — Hardware Requirements & GPU Compatibility
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PowerLM 3B is a 3.5B-parameter open language model from ibm-research. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 3.16 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~207 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~23 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run PowerLM 3B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve ibm-research/PowerLM-3b --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (8 GB headroom) · Q4_K_M
- Generation speed
- ~188 tok/s
- generation speed
- Cost per 1M output tokens
- $0.18
- per 1M output tokens
How Much VRAM Does PowerLM 3B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.5 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~257 tok/s | |
| 2.8 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~237 tok/s | |
| 3.2 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~207 tok/s | |
| 3.6 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~184 tok/s | |
| 4.0 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~166 tok/s | |
| 4.6 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~143 tok/s | |
| 8.1 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~81 tok/s |
Selected Q4_K_M
4-bit medium quantization3.9GBat 4K context
- Weights
- 2.1 GB
- KV cache
- 1.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.1 GB free · ~207 tok/s
≥50 tok/s needs ≥243 GB/s · RTX 5090 ~369, RTX 3090 Ti ~207
Which Hardware Can Run PowerLM 3B?
Q4_K_M · 3.2 GBPowerLM 3B (Q4_K_M) requires 3.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 4K context window can add up to 0.8 GB, bringing total usage to 3.9 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run PowerLM 3B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.1 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~156Simulate output on RTX 3080RTX 2080 Ti~127Simulate output on RTX 2080 TiRTX 3070 Ti~125Simulate output on RTX 3070 TiM3*8 GB~23Simulate output on iMac 24" M3 (8 GB)Orin Nano~21Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~15Simulate output on iPhone 1720 GB
fits · 16.1 GB free · up to 4K ctx
1 GPU
RX 7900 XT~152Simulate output on RX 7900 XT32–40 GB
fits · 28.1 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~369Simulate output on RTX 5090M5 Max36 GB~102Simulate output on Mac Studio M5 Max (36 GB)M532 GB~34Simulate output on MacBook Air 13" M5 (32 GB)Orin~42Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~28Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite 4.0 MicroIBM | — | 3.7 GB | ~261 | same size |
| Qwen2.5 VL 3B InstructAlibaba | — | 3.2 GB | ~249 | same size |
| Deepseek Vl2 TinyDeepSeek | — | 9.4 GB | ~152 | same size |
| Tiny Aya GlobalCohere | — | 9.4 GB | ~89 | same size |
| Phi Tiny MoE InstructMicrosoft | — | 3.1 GB | ~309 | same size |
| MiniMax M3 EAGLE3Inferact | — | 2.7 GB | ~284 | same size |
Specifications
- Publisher
- ibm-research
- Parameters
- 3.5B
- Architecture
- GraniteForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 49,152
- Release Date
- 2024-08-14
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does PowerLM 3B need?
PowerLM 3B requires 3.2 GB of VRAM at Q4_K_M, or 8.1 GB at BF16. Full 4K context adds up to 0.8 GB (3.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.5B × 4.8 bits ÷ 8 = 2.1 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 1.8 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for PowerLM 3B?
For PowerLM 3B, Q4_K_M (3.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.5 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run PowerLM 3B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run PowerLM 3B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run PowerLM 3B locally?
Yes — PowerLM 3B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is PowerLM 3B?
At Q4_K_M, PowerLM 3B can reach ~1519 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~207 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 ÷ 3.16 × 0.65 = ~1646 tok/s
Estimated speed at Q4_K_M (3.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of PowerLM 3B?
At Q4_K_M, the download is about 2.11 GB. The full-precision BF16 version is 7.02 GB. The smallest option (Q2_K) is 1.49 GB.
Which GPUs can run PowerLM 3B?
52 consumer GPUs can run PowerLM 3B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run PowerLM 3B?
163 devices with unified memory can run PowerLM 3B at Q4_K_M (3.2 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.