BMInf: Running 10B-Parameter Language Models on a GTX 1060
Efficient Inference for Big Models
At a glance
- What is it?
- BMInf is a Python inference package from OpenBMB that quantizes transformer weights so large pretrained language models fit on consumer GPUs. It is a narrow tool with a real speed trade-off on the decoder side, and the README is candid about both.
- Who is it for?
- Adopt BMInf if you need to serve a transformer model larger than your GPU memory allows, you are on a single NVIDIA card with compute capability 6.1 or higher, and your workload is encoder-heavy or tolerant of slow token-by-token decoding.
- Can I use it commercially?
- Yes. Apache-2.0 is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 70 days ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The memory wall BMInf is built to climb
A 10-billion-parameter model in fp16 needs roughly 20GB just for weights, before activations and the KV cache. That puts most large pretrained language models out of reach of a single consumer card. BMInf's stated goal is to close that gap: the README claims support for models above 10 billion parameters on a single NVIDIA GTX 1060 with 6GB of VRAM, and lists a 16GB system memory floor alongside it. The intended user is not a research lab with an A100 cluster. It is someone with one gaming GPU, or a small deployment box, who wants to run a model that would otherwise require renting hardware. The package is Python, Apache-2.0 licensed, and distributed on pip as bminf.
QuantizedLinear and TransformerBlockList: the two substitution points
BMInf does not reimplement a transformer. It replaces two PyTorch primitives. torch.nn.Linear becomes bminf.QuantizedLinear, which is where the weight compression happens, and torch.nn.ModuleList becomes bminf.TransformerBlockList, which takes a list of blocks and a CUDA device index. The README's quick start shows the intended path: build your model on CPU, load the state_dict first, then call bminf.wrapper(model) inside a torch.cuda.device context. The ordering matters. The README explicitly says to load the checkpoint before applying the wrapper, which implies the wrapper transforms an already-populated module tree rather than accepting weights afterward. If your model does not fit the wrapper's assumptions, the README offers the manual route: swap the two module types yourself. That is the whole integration surface, and it is small, which is a point in its favour for anyone who has fought with inference frameworks that want to own the model definition.
Installation and the hardware gate
Installation is one command, pip install bminf, or python setup.py install from a source checkout. Dependencies install automatically and the README lists three: python 3.6 or newer, torch 1.7.1 or newer, and cpm_kernels 1.0.9 or newer. CUDA must be 10.1 or higher. The hardware requirement is where the tool draws a hard line. GPUs need compute capability 6.1 or above, and the README points to the CUDA GPU table to check. The minimum configuration is a GTX 1060 6GB with 16GB of system memory; the recommended configuration is a V100 16GB with 24GB of memory. If your card predates compute capability 6.1, or you are on AMD or Apple silicon, this package does not apply to you and no amount of configuration will change that. The project's own README badge still reads version 1.0.0 even though the latest release is 2.0.1, a small sign that the front page lags the code.
Where the speed actually goes: encoder versus decoder
The performance table is the most useful part of the README because it separates the two workloads. On a GTX 1060, the CPM2 encoder runs at 718 tokens per second while the decoder manages 4.4. On a V100 the split is 2966 against 20. On an A100, 4365 against 26. The comparison rows are the interesting ones: plain PyTorch on a V100 decodes at 3 tokens per second and on an A100 at 7, against BMInf's 20 and 26. So the decoder gap is real but it is a constant-factor improvement, not an order of magnitude, and the absolute numbers are low. Twenty-six tokens per second means a 200-token response takes roughly eight seconds. The encoder numbers are where BMInf looks strong, but the table has no PyTorch encoder baseline, so the encoder speedup is not directly comparable from the supplied material. Treat the decoder comparison as the load-bearing evidence and the encoder figures as raw throughput.
What the 2.0 release changed, and what that implies for version pinning
The What's New section records that BMInf 2.0.0, dated 2022/07/31, extended the package to any transformer-based model. Before that, support was narrower. The 1.0.0 release in December 2021 removed a cupy dependency and added PyTorch backpropagation support. The README also notes that documentation for BMInf-1 lives in an old_docs directory, and that CPM-1, CPM-2 and EVA examples are promised but not yet present at the time of writing. That last point is a practical constraint: if you are trying to run one of those specific models, the README tells you the examples are coming, which means you are working from the API surface and the paper rather than a worked end-to-end script. The paper reference is Han et al., ACL 2022 System Demonstrations, for anyone who needs the design rationale the README omits.
The case against BMInf, and what to use instead
BMInf is the wrong tool when your GPU already fits the model. The README concedes this indirectly: it says that on a V100 or A100, BMInf still improves on the existing PyTorch implementation, but the decoder numbers show that improvement is a factor of roughly four to seven, not a change in feasibility. If you are decoding interactively on an A100, a plain PyTorch forward pass at 7 tokens per second versus BMInf at 26 is a real difference, but both are slow enough that batching and caching strategy will matter more than the framework. The more direct alternative for memory-constrained inference is a general quantization or offloading library that keeps the standard PyTorch module interface, so you do not have to reason about whether wrapper handles your architecture. BMInf's advantage over that class of tool is that it is purpose-built for the OpenBMB model family and the substitution points are documented in two lines of code. Its disadvantage is the same narrowness: if wrapper does not fit, you are manually rewriting ModuleList and Linear call sites across your model definition, and there is no fallback described in the README for models that use neither.
Maintenance, licensing and what to check before you commit
The package is Apache-2.0, which permits commercial use and modification provided you retain the licence and notices; that is a summary, not legal advice, and anyone shipping it in a product should read the LICENSE file in the repository. The last release is 2.0.1 from January 2023, with the repository's last push recorded as July 2026, so the code is not abandoned but the release cadence is slow. The dependency floor is the maintenance risk: torch 1.7.1 and CUDA 10.1 are old baselines, and cpm_kernels is a separate package with its own version requirement at 1.0.9 or newer. Before adopting, run the benchmark scripts the README names, benchmark/cpm2/encoder.py and benchmark/cpm2/decoder.py, on your own card. Those two files are the only way to know whether the published numbers transfer to your hardware, and they are the specific next step this project gives you.
Editorial conclusion
Adopt BMInf if you need to serve a transformer model larger than your GPU memory allows, you are on a single NVIDIA card with compute capability 6.1 or higher, and your workload is encoder-heavy or tolerant of slow token-by-token decoding. Do not adopt it if you have an A100 or V100 and need fast generation, because the README's own decoder numbers show PyTorch on an A100 reaching 7 tokens per second against BMInf's 26, a gap that narrows but does not vanish, and the encoder path is where the package earns its place. Before committing, verify three things on your own hardware: that your model's architecture survives bminf.wrapper without manual substitution of TransformerBlockList and QuantizedLinear, that your CUDA version is at least 10.1, and that your decoding latency budget can absorb the decoder throughput shown in the benchmark table for your GPU class.
Community notes