Choosing the best GPU for LLM workloads in 2026 comes down to three questions: how much memory your model needs, how many people will use it at the same time, and whether you only run the model or also train it. We compare NVIDIA HGX B300, HGX B200, H200, RTX PRO 6000 Blackwell Server Edition and Intel Gaudi 3 – no marketing charts, just what matters when you actually deploy.
We write this as the people who supply and operate these systems. As a Supermicro and NVIDIA partner, we install GPU servers in our Tier III data centre in Poland and provide them as a service or in colocation. So the focus here is on memory, platform, power and cooling rather than peak FLOPS from datasheets. Hardware details are current as of November 2026.
The short answer: which GPU for LLMs
If you want a starting point before reading the rest:
- RTX PRO 6000 Blackwell Server Edition (96 GB) – the best price-to-capability ratio for inference, RAG and fine-tuning mid-size models. One to eight cards per server.
- HGX B200 (8 × 180 GB) – production inference of large models with many concurrent users, plus training.
- HGX B300 (8 × 288 GB) – the largest models (1T+ parameters), training and the highest compute density. Needs serious power and cooling planning.
- H200 (141 GB) – the previous (Hopper) generation, still widely deployed. Sensible when your software and processes are already proven on it.
- Intel Gaudi 3 (8 × 128 GB) – a cost-efficient alternative for training and inference if your stack is built on PyTorch and vLLM.
- DGX Spark (128 GB) – prototyping, R&D, small teams. Not for high-concurrency production serving.
Best GPU for LLM compared: memory, platform, use case
For LLMs, memory is the parameter that matters most: capacity decides whether the model fits at all, and bandwidth decides how quickly it produces tokens.
| Accelerator | Memory per GPU | Memory type | Platform / form factor | Best use |
|---|---|---|---|---|
| NVIDIA HGX B300 (Blackwell Ultra) | 288 GB (approx. 2.3 TB per 8-GPU system) | HBM3e | 8 GPUs, 1.8 TB/s NVLink, up to 1,100 W per GPU; Supermicro 8U air-cooled, 4U and 2-OU OCP liquid-cooled; ConnectX-8 800 Gb/s | Training, 1T+ models, very high-concurrency inference |
| NVIDIA HGX B200 | 180 GB (1,440 GB per system) | HBM3e | 8 GPUs, 1.8 TB/s NVLink; Supermicro 8U/10U air-cooled or 4U liquid-cooled | Production inference of large models, training and fine-tuning |
| NVIDIA H200 | 141 GB | HBM3e | Hopper generation, 8-GPU systems | Existing estates, inference of 70B–400B models |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | 96 GB | GDDR7 ECC, approx. 1.6 TB/s | PCIe card, up to 600 W; Supermicro 5U with up to 8 cards, MGX-based 4-GPU system | Inference, RAG, mid-size fine-tuning, multi-tenant environments |
| Intel Gaudi 3 | 128 GB (1,024 GB per system) | HBM2e | 8 OAM accelerators (HL-325L air-cooled, HL-335 liquid-cooled), 6 on-board 800 GbE ports; Supermicro SYS-822GA-NGR3 | Cost-efficient training and inference on PyTorch and vLLM |
| NVIDIA DGX Spark (for reference) | 128 GB, shared with the CPU | LPDDR5X, 273 GB/s | 150 × 150 × 50.5 mm desktop unit, 240 W power supply | Prototyping, fine-tuning up to 70B, private inference for a small team |
We deliberately leave out memory bandwidth and compute figures for B200 and B300. They depend on configuration and precision mode, and comparing peak numbers across different datasheets tends to mislead more than it helps. Qualitatively: HBM3e in HGX systems offers clearly higher bandwidth than the GDDR7 on the RTX PRO 6000, and GPUs inside an HGX node talk to each other over NVLink. RTX PRO 6000 cards communicate over PCIe, which is rarely a problem for inference but does become one when training large models across many GPUs.
Inference vs training: different bottlenecks
Inference: memory capacity and bandwidth
To generate each token, the GPU has to read the model weights from memory (all of them for a dense model, the active ones for MoE). That is why single-user generation speed is limited mainly by memory bandwidth rather than raw compute. Capacity, in turn, decides whether there is room next to the weights for the KV cache of many parallel conversations. Engines such as vLLM batch requests continuously and make much better use of the GPU under concurrent load – but only if there is free memory to work with.
In practice, a server with a few RTX PRO 6000 cards gives you a lot of headroom for inference of models up to around 120B parameters at reasonable cost. HBM-based HGX systems pay off when the model is very large, or when you serve many users at once and response latency matters.
Training and fine-tuning: compute, interconnect, memory
Training keeps not just the weights in memory, but also gradients, optimiser states and activations – several times more than inference. GPUs exchange data constantly, so NVLink inside the node (1.8 TB/s on HGX B200 and B300) and networking between nodes (ConnectX-8 800 Gb/s on B300, six 800 GbE ports built into the Gaudi 3 baseboard) matter a great deal. LoRA/QLoRA fine-tuning of mid-size models runs comfortably on RTX PRO 6000. Full training of large models is a job for HGX or Gaudi 3.
Which GPU for which model size
There is no single best GPU for LLM workloads – the right choice follows from model size. Model weights take roughly: number of parameters × bytes per parameter (FP8 = 1 byte, 4-bit ≈ 0.5 bytes). Add the KV cache and 10–20% overhead on top. We walk through the full method with worked examples in how much VRAM does an LLM need. The table below is an indicative sizing based on models available in November 2026 – the market changes every month.
| Model size (examples) | Weights: FP8 / 4-bit (approx.) | Sensible configuration |
|---|---|---|
| 7–12B (Bielik 11B, PLLuM-12B, Gemma 4 12B) | 7–12 GB / 4–6 GB | One 24–48 GB GPU; 1× RTX PRO 6000 for many users |
| 24–32B (Qwen3.8-27B, Gemma 4 31B, Devstral Small 2) | 24–32 GB / 12–16 GB | 1× RTX PRO 6000; DGX Spark |
| 70B (Llama-PLLuM-70B) | 70 GB / 35 GB | 1–2× RTX PRO 6000; 1× H200 or B200 |
| Around 120B, incl. MoE (gpt-oss-120b, Mistral Small 4, Devstral 2) | 117–125 GB / 59–63 GB | 1× RTX PRO 6000 (4-bit), 2× RTX PRO 6000 (FP8); DGX Spark (4-bit) |
| Around 400B (Llama 4 Maverick) | 400 GB / 200 GB | 4× RTX PRO 6000 (4-bit), 8× RTX PRO 6000 (FP8); HGX H200/B200; 2-unit DGX Spark cluster for prototyping |
| 700B–1T (DeepSeek-V4.1-Flash, GLM-5.3, Mistral Large 4) | 750–1,000 GB / 375–500 GB | HGX B200 or B300; at 4-bit also 8× RTX PRO 6000; 4-unit DGX Spark cluster (up to approx. 700B) for prototyping |
| 1.6–2.8T (DeepSeek-V4-Pro, Qwen3.8-2.4T, Kimi K3) | 1,650–2,780 GB / 825–1,390 GB | HGX B300 (4-bit; for approx. 1.65T also FP8); HGX B200 at 4-bit for approx. 1.65T; larger models at FP8 need several nodes |
Keep in mind that MoE models must hold all their parameters in memory, even though only the active ones are computed for each token. A 1T-A52B model needs as much memory as a trillion-parameter model, but computes faster than a dense model of that size. If you are still choosing a model, start from what it needs to do and size the hardware last.
When DGX Spark is enough
NVIDIA DGX Spark is a compact unit with the GB10 Grace Blackwell Superchip and 128 GB of unified memory. According to NVIDIA, it handles inference of models up to 200B parameters and fine-tuning up to 70B. Two units linked with a QSFP cable give you 256 GB (models up to approx. 400B), and four units on a 200 GbE switch give 512 GB (up to approx. 700B).
There is one limitation, and it matters: 273 GB/s of memory bandwidth is a fraction of what HBM delivers. Large dense models therefore generate tokens slowly, and the gap widens with many concurrent users. DGX Spark is enough for prototyping, R&D, fine-tuning and private inference for a small team. For production with many users, an RTX PRO 6000 server or HGX is the better fit. See NVIDIA DGX Spark as a Service and DGX Spark vs RTX PRO 6000 vs HGX B200 for the details.
Power and cooling: the cost people forget
Choosing a GPU also means choosing a data centre. A single B300 GPU draws up to 1,100 W, so an eight-GPU system is 8.8 kW for the GPUs alone – before CPUs, memory, networking and fans. According to Supermicro, an air-cooled rack fits up to 32 B200 GPUs, while a liquid-cooled rack fits up to 96. Even a server with eight RTX PRO 6000 cards (up to 600 W each) means up to 4.8 kW for the cards alone.
Systems like these do not belong in an office server room with comfort air conditioning. They need dedicated power, redundancy and – for dense Blackwell deployments – liquid cooling. We compare both approaches in GPU server cooling: air vs liquid. If you buy your own hardware, you can place it in our server colocation, where liquid-cooled racks are available.
Buy, rent or colocate?
You have three routes, and we handle all of them in one place:
- GPU as a Service – rent a GPU server (from a VPS with a dedicated GPU, through bare metal with up to 8 GPUs, to multi-node clusters) with no hardware investment.
- Purchase and colocation – we supply a Supermicro server with NVIDIA GPUs or Intel Gaudi 3, install it in our data centre and operate it.
- Ready-made packages for smaller models – our DeepSeek GPU servers come in several sizes and also run Llama, Qwen, Mistral, Gemma or Bielik.
Blackwell systems (B200, B300, RTX PRO 6000) and Gaudi 3 are priced on a custom quote, depending on configuration and contract term. In every option you choose between full 24/7 administration and root access for self-management. For the fundamentals behind GPU performance, see our explainer on GPU computing power.
FAQ: frequently asked questions
What is the best GPU for running an LLM in a company?
For most companies a sensible start is a single RTX PRO 6000 (96 GB), or DGX Spark for prototyping. An RTX PRO 6000 runs models up to around 30B at full BF16 precision, 70B at FP8 and around 120B with 4-bit quantisation. Smaller models (up to around 12B) also run on cheaper 24–48 GB GPUs.
Is NVIDIA B200 better than H200?
B200 is the newer (Blackwell) generation with more memory: 180 GB HBM3e versus 141 GB on H200. It fits more of the model and more KV cache per GPU. H200 is still widely deployed and well supported by software, so it can make sense if the price is right or you already run Hopper infrastructure.
How many GPUs do I need for a 70B model?
At FP8, a 70B model’s weights take about 70 GB, so they fit on one RTX PRO 6000 – but with little room left for context. For more than a handful of users, plan for two cards or a single H200 or B200. With 4-bit quantisation (about 35 GB), one 96 GB card leaves plenty of headroom.
Is the RTX PRO 6000 suitable for production?
Yes. It is a server card with ECC memory, designed for continuous operation, and it works well for production inference, RAG and multi-tenant setups. Its weak point is the lack of NVLink, so for full training of large models HGX systems are the better choice.
Does Intel Gaudi 3 support vLLM and PyTorch?
Yes. Gaudi 3 uses the Intel Gaudi software suite, supports PyTorch, and vLLM supports Gaudi. Before committing, check that the specific models and features you rely on are already supported on the platform.
Not sure which GPU fits your model? Tell us which model you want to run and for how many users, and we will put together a configuration and quote for a GPU server in our data centre. Get in touch.