No. More memory decides which models you can load, but once a model fits, speed depends mostly on memory bandwidth for generating tokens and on compute for reading prompts and serving many users. That is why the 32 GB RTX 5090 and the 96 GB RTX PRO 6000 Blackwell run a model that fits both at almost the same speed: they have the same 1,792 GB/s of bandwidth. It is also why the GB300's 748 GB headline needs care. Only 252 GB of it is HBM3e at 7.1 TB/s; the other 496 GB runs at 396 GB/s, slower than either graphics card.
What sets the speed limit for an AI model?
Generating each new token means reading the model's active weights from memory once. So for a single user, the ceiling is roughly memory bandwidth divided by the bytes read per token. Real results land below that ceiling, typically around 60 to 80% for dense models. The table shows our calculated ceilings, weights only.
| Model (bytes read per token) | RTX PRO 6000 WE (1,792 GB/s) | GB300 HBM3e (7,100 GB/s) | GB300 LPDDR5X (396 GB/s) | DGX Spark GB10 (273 GB/s) |
|---|---|---|---|---|
| Llama 3.3 70B at FP8 (about 70.6 GB) | About 25 tok/s | About 101 tok/s | About 5.6 tok/s | About 3.9 tok/s |
| Llama 3.3 70B at 4-bit (about 35.3 GB) | About 51 tok/s | About 201 tok/s | About 11 tok/s | About 7.7 tok/s |
| Qwen3-235B-A22B at FP8 (about 22 GB active) | About 81 tok/s, but needs four cards | About 323 tok/s | About 18 tok/s | About 12 tok/s |
Source: our calculation (bandwidth ÷ bytes read per token, single stream, weights only, ignoring KV cache and multi-card overhead). Bandwidth from NVIDIA specifications. Model sizes from Hugging Face checkpoints. These are theoretical ceilings, not benchmarks.
Measured results follow the same pattern. hardware-corner measured Llama 3.3 70B at 4-bit (Q4_K) at 32 tokens per second on one RTX PRO 6000 at a 4K context, about 75% of the ceiling for that file size.
So why doesn't the extra memory make the RTX PRO 6000 faster than an RTX 5090?
Because the bandwidth is identical. When a model fits in 32 GB, the two cards trade wins. On Qwen3 32B at 4-bit in llama.cpp, hardware-corner measured 43 tok/s on the RTX 5090 and 39 on the RTX PRO 6000. In StorageReview's tests the 5090 was slightly faster on SDXL images and 22% faster in V-Ray. Memory only pays off when the model, its context or its users need more than 32 GB. Then the gap becomes huge: GamersNexus measured Gemma 3 27B at 29 tok/s on the RTX PRO 6000 against 5 on the 5090, and the 5090 cannot load gpt-oss-120b at all. Our RTX PRO 6000 vs RTX 5090 comparison covers this in full.
Capacity is a gate. Once you're through it, bandwidth sets the pace.
Why is the GB300's 748 GB not 748 GB of fast memory?
The GB300 joins 252 GB of HBM3e on the GPU to 496 GB of LPDDR5X on the Grace CPU, coherently, over NVLink-C2C. Weights in HBM3e stream at 7.1 TB/s, about four times an RTX PRO 6000. Weights that spill into LPDDR5X stream at 396 GB/s, about a fifth of the graphics card's bandwidth. StorageReview ran GLM-5.2 with about 215 GB of experts in Grace memory at about 35 tokens per second for one user. That's usable, but far from what the same model would do in HBM.
So the useful comparison is:
- Up to 96 GB: both run the model; the GB300 is faster because of HBM bandwidth and compute.
- 96 to 252 GB: the GB300 runs it in fast memory on one GPU. RTX PRO 6000 cards need two or three cards and splitting over PCIe.
- 252 to about 700 GB: the GB300 loads it, with part of it in slower LPDDR5X. RTX PRO 6000 cards need four to eight cards.
Our explainers on HBM3e vs LPDDR5X in GB300 and why DGX Station's 748 GB isn't all VRAM go deeper.
Where does compute matter more than memory?
Two places: reading long prompts and serving many users. Prefill, the step that processes your prompt, is compute-bound. So is batched serving, where one pass over the weights produces tokens for many users at once. Here the GB300's 20 PFLOPS of sparse FP4 is about five times the RTX PRO 6000's 4,000 AI TOPS (also sparse). Published results reflect it:
- GPT-OSS-120B at 128 concurrent requests: 9,280 output tok/s on a GB300 system (ASUS ET900N G3) against 3,064 on one RTX PRO 6000, about 3.0 times (StorageReview, vLLM).
- The same test on DGX Spark: 500 tok/s. The RTX PRO 6000 was about 6.1 times faster, close to its 6.6 times bandwidth advantage, even though the Spark has more memory (128 GB vs 96 GB).
That last pair is the clearest answer to the title. The Spark has more memory than the RTX PRO 6000 and is far slower. Our GB10 vs RTX PRO 6000 post explains when the Spark's capacity is still worth having.
When does more memory genuinely win?
- Long context: KV cache grows with every token. Llama 3.3 70B needs about 43 GB of BF16 KV cache at 128K tokens for one user, by our calculation from its configuration. That can't sit beside 70 GB of FP8 weights on one 96 GB card.
- Many users: ten users at 32K tokens on the same model need about 107 GB of KV cache before weights.
- Higher precision: keeping a model at FP8 or BF16 rather than 4-bit can matter for accuracy. Capacity lets you choose.
- Avoiding splits: a model that fits on one GPU avoids PCIe traffic between cards, which our multi-GPU without NVLink post covers.
- Non-AI work: big CFD meshes, renders and scenes simply fail or slow to a crawl if they spill out of VRAM. Ansys's guidance puts Fluent's GPU solver at 1.0 to 5.6 GB per million cells depending on mesh and precision.
How should you size a system, then?
- Work out weights at your chosen precision, plus KV cache for your context and users, plus about 10 to 20% headroom.
- Pick the smallest memory tier that holds it entirely in fast memory.
- Within that tier, rank options by bandwidth for single-user speed, and by compute for many users and long prompts.
- Only then compare prices. Our 96GB VRAM sizing guide and RTX PRO 6000 vs GB300 comparison help with steps 2 and 4.
Buying in Singapore
SourceIT lists the RTX PRO 6000 Blackwell Workstation Edition at S$29,100 here (listed 6 Nov 2026; prices change, so ask for a current quotation), with other cards in our NVIDIA graphics cards collection. For more memory at lower bandwidth, the NVIDIA DGX Spark 4TB is S$9,300 here and the ASUS Ascent GX10 1TB S$10,350 here. DGX Station GB300 systems such as the MSI XpertStation WS300 and GIGABYTE W775-V10-L01 are price on request. Tell us your models, context lengths and user count, and we'll size the right tier. Use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.
Frequently asked questions
Does more VRAM make AI models run faster? Not once the model fits. Token generation speed then depends mainly on memory bandwidth, and serving many users depends on compute. More VRAM lets you load bigger models, longer contexts and more users.
Why is the RTX 5090 as fast as the RTX PRO 6000 on some models? Both have 1,792 GB/s of memory bandwidth. On models that fit in 32 GB, published tests show them within about 0 to 25% of each other, and the 5090 sometimes wins.
Is GB300's 748 GB all fast memory? No. 252 GB is HBM3e at 7.1 TB/s; 496 GB is LPDDR5X at 396 GB/s. Models that spill into LPDDR5X slow down considerably.
Is DGX Spark faster than an RTX PRO 6000 because it has 128 GB? No. On GPT-OSS-120B at 128 concurrent, StorageReview measured 500 tok/s on DGX Spark and 3,064 on an RTX PRO 6000, which has about 6.6 times the bandwidth.
How do I estimate token speed? Divide memory bandwidth by the bytes read per token (roughly the active weights). Expect real single-user results at about 60 to 80% of that ceiling for dense models.
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Researched and verified by the SourceIT team for Singapore buyers.
