What GPU do I need to run RedHatAI/Qwen2-1.5B-Instruct-FP8?
1.5B parameters, published in F8_E4M3. View on Hugging Face
Set up Qwen2-1.5B-Instruct-FP8Qwen2-1.5B-Instruct-FP8 is published by RedHatAI on Hugging Face, with 15,732 downloads and 0 likes to date. It's a Qwen2ForCausalLM model built for text-generation, published natively in F8_E4M3.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 1.4 GB | 1.7 GB | RTX 4080 | 1 | $0.173/hr |
| INT4 (quantized) | 0.7 GB | 0.9 GB | RTX 3060 | 1 | $0.110/hr |
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run Qwen2-1.5B-Instruct-FP8 at its published (F8_E4M3) precision: 1× RTX 4080, at $0.173/hr per GPU ($0.173/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Qwen2-1.5B-Instruct-FP8: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Grouped-query attention: every layer caches keys and values for a few shared KV heads. Cached per token: 28,672 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 0.11 GB |
| 32K | 0.88 GB |
Computed from the layer, head and window counts in the model's own configuration (RedHatAI/Qwen2-1.5B-Instruct-FP8), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
Qwen2-1.5B-Instruct-FP8: common questions
How much VRAM does Qwen2-1.5B-Instruct-FP8 need?
1.7 GB at FP8 (native), 0.9 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 1.4 GB of weights plus inference overhead is the whole requirement.
Is Qwen2-1.5B-Instruct-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 1.7 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 0.9 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
How many copies of Qwen2-1.5B-Instruct-FP8 fit on one RTX 4080?
9, by VRAM alone. That card carries 16.0 GB and one copy needs 1.7 GB at FP8 (native), on a live rate of $0.173/hr for the whole card. Throughput is not modelled here, so 9 copies is not 9 times the requests served.
Does quantizing Qwen2-1.5B-Instruct-FP8 lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is one RTX 4080 at $0.173/hr. At INT4 (quantized) it drops to one RTX 3060 at $0.110/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Qwen2 models
- Qwen2-1.5B-Instruct (1.5B, BF16)
- Qwen2-1.5B (1.5B, BF16)
- gte-Qwen2-1.5B-instruct (1.8B, F32)
- Qwen2-0.5B (494M, BF16)
- Qwen2-0.5B-Instruct (494M, BF16)
Alternatives at this size
Other models for text-generation within about a third of Qwen2-1.5B-Instruct-FP8's 1.5B parameters, from other model lines.
- Llama-3.2-1B-Instruct (1.2B, BF16)
- OpenELM-1_1B-Instruct (1.1B, BF16)
- OLMo-2-0425-1B (1.5B, F32)
- MiniCPM5-1B (1.1B, BF16)
- LFM2.5-1.2B-Instruct (1.2B, BF16)
More on Qwen2-1.5B-Instruct-FP8
Fits on an 8 GB GPU at FP8: every model that fits in 8 GB.
Related reading: RTX 4080 pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
