Param2 17B A2.4B Thinking — Hardware Requirements & GPU Compatibility
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Param2 17B A2.4B Thinking is a 17.2B-parameter open language model from bharatgenai. It supports a context window of up to 4,096 tokens. At BF16 it needs about 34.69 GB of VRAM — see which GPUs and Macs can run it below.
Param2 17B A2.4B Thinking runs on a 48 GB+ Mac or a workstation GPU
Best local pick → Mac Studio M5 Max (48 GB) · BF16 · 34.8 GB · ~62 tok/s
Macs from 48 GB (BF16 · ~62 tok/s)
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Param2 17B A2.4B Thinking
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 bharatgenai/Param2-17B-A2.4B-Thinking --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 A40 (48 GB) (13 GB headroom) · BF16
- Generation speed
- ~80 tok/s
- generation speed
- Cost per 1M output tokens
- $1.22
- per 1M output tokens
How Much VRAM Does Param2 17B A2.4B Thinking Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 34.7 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 34.7 GB |
Selected BF16
Brain floating point 1634.8GBat 4K context
- Weights
- 34.3 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 10.8 GB more
Or .
Which Hardware Can Run Param2 17B A2.4B Thinking?
BF16 · 34.7 GBParam2 17B A2.4B Thinking (BF16) requires 34.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 46+ GB is recommended. Using the full 4K context window can add up to 0.1 GB, bringing total usage to 34.8 GB. No consumer GPU has enough memory. 67 devices with unified memory can run Param2 17B A2.4B Thinking, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (48 GB).
48 GB
48 GB tier →fits · 13.2 GB free · up to 4K ctx
2 Mac classes · 1 device family
M5 Max48 GB~62Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~35Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~27Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Similar models
3| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Kimi VL A3B InstructMoonshot AI | — | 10.7 GB | ~208 | same size |
| Moonlight 16B A3BMoonshot AI | — | 10.1 GB | ~229 | same size |
| Deepseek Moe 16B ChatDeepSeek | — | 11.1 GB | ~231 | same size |
Specifications
- Publisher
- bharatgenai
- Parameters
- 17.2B
- Active parameters
- 2.6B per token
- Architecture
- Param2MoEForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 128,008
- Release Date
- 2026-02-16
Frequently Asked Questions
How much VRAM does Param2 17B A2.4B Thinking need?
Param2 17B A2.4B Thinking requires 34.7 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 17.2B × 16 bits ÷ 8 = 34.3 GB
KV Cache + Overhead ≈ 0.4 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 ≈ 0.5 GB (at full 4K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run Param2 17B A2.4B Thinking?
No — Param2 17B A2.4B Thinking requires at least 34.7 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
Can I run Param2 17B A2.4B Thinking on a Mac?
Yes — Mac Studio M5 Max (48 GB) and 55 other Macs can run Param2 17B A2.4B Thinking. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 34.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Param2 17B A2.4B Thinking locally?
Yes — Param2 17B A2.4B Thinking can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Param2 17B A2.4B Thinking?
At BF16, Param2 17B A2.4B Thinking can reach ~190 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 5.2 GB active ÷ (8000 × 0.65) = 0.99 ms, plus 21 layers × 0.055 ms = 1.16 ms, so 1000 ÷ 2.15 ms = ~465 tok/s
Estimated speed at BF16 (34.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Param2 17B A2.4B Thinking?
At BF16, the download is about 34.30 GB.
Which GPUs can run Param2 17B A2.4B Thinking?
No single consumer GPU has enough VRAM to run Param2 17B A2.4B Thinking at BF16 (34.7 GB). Multi-GPU or professional hardware is required.
Which devices can run Param2 17B A2.4B Thinking?
67 devices with unified memory can run Param2 17B A2.4B Thinking at BF16 (34.7 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.