B300 Vs B200: Why Memory Decides Long-Context Inference

Summary

B300 beats B200 on long-context inference that is memory-bound, and B200 holds up when the model fits. Per 8-GPU system, NVIDIA's HGX specifications give B300 2.1 TB of HBM3e against 1.4 TB, 1.5x the dense FP4 compute and 2x attention throughput. FP8 and NVLink bandwidth are equal. Choose B300 if customers run frontier models at long context, and B200 if workloads fit in 1.4 TB or need FP64.

FP64 is the double-precision math used in scientific simulation. NVIDIA lists it at 296 TFLOPS per 8-GPU system on B200 against 10 TFLOPS on B300. Dense FP4 runs 108 PFLOPS on B300 against 72 PFLOPS on B200, per the same NVIDIA table.

Comparison Table

B300 adds 0.7 TB of memory per 8-GPU system and keeps FP8 and NVLink level with B200, per NVIDIA's HGX table. For B200's per-GPU figures, NVIDIA's HGX reference architecture lists 180 GB of HBM3e at up to 8 TB/s, configurable up to 1 kW per GPU. NVIDIA's DGX B300 user guide lists 288 GB per GPU and 15 kW system max, and the DGX B200 datasheet lists ~14.3 kW max.

Dell's PowerEdge configurations in the InfoHub test below run 1,000 W per GPU on B200 and 1,100 W per GPU on B300 (OEM figures). NVIDIA's HGX page lists 2.1 TB per B300 system, and the DGX B300 user guide's 8 x 288 GB works out to 2.3 TB. The table uses the HGX totals. Full spec pages sit in our NVIDIA B300 hardware reference and NVIDIA B200 hardware reference. For how NVIDIA's own DGX builds differ from partner HGX servers, see HGX vs DGX systems.

Memory and Long-context Inference: Where B300 Pulls Ahead

B300 cut time to first token (TTFT, the wait before the first output token) 5.1x in Dell's 2026 InfoHub test on Kimi K2.6 at FP4. Dell ran 16K input and 2K output tokens at concurrency 128 on both platforms. On that Kimi K2.6 workload, TTFT fell from 97.9 s to 19.2 s and throughput rose 28.4%, from 9,060 to 11,632 tokens per second.

Dell reports an identical software stack on both platforms (vLLM 0.8.x, CUDA 12.8), so the gap comes from the hardware. The KV cache, the memory a model keeps for each active request's context, grows with context length and with the number of concurrent requests. When model weights plus KV cache press against 1.4 TB per 8-GPU system, the server shrinks batches or queues requests, and TTFT climbs.

B300's extra 0.7 TB per system and 2x attention throughput let it hold more concurrent long-context requests at once. For an operator, that translates into sellable capacity per server: more long-prompt sessions served at an acceptable TTFT. The headroom matters most when your offtake customers serve frontier models with long prompts, agents or retrieval.

Ask your offtake customer for its model, target context length and expected concurrency before you choose. Those three inputs show whether weights plus KV cache will press against 1.4 TB per 8-GPU system or sit well inside it. If the offtake contract lets the customer move to longer contexts or larger models mid-term, B300's headroom keeps the servers sellable for that work.

Where B200 Holds up or Wins

B300 trailed B200 by 13–17% in Dell's test on Nemotron NVFP4 at 16K–32K context and concurrency 128, where the model fit in 1.4 TB. Once the weights and KV cache fit in memory, B300's extra capacity has less work to do. Per NVIDIA's HGX table, FP8 is equal at 72 PFLOPS sparse per 8-GPU system, and NVLink is identical at 1.8 TB/s per GPU. Workloads that fit in memory gain little from B300.

FP64 falls from 296 TFLOPS on B200 to 10 TFLOPS on B300 per 8-GPU system, per NVIDIA's HGX table. Scientific simulation and HPC customers who need double precision belong on B200. Typical B200-fit workloads include:

• Models that fit in 1.4 TB per 8-GPU system with room for the KV cache
• Short-to-medium context inference
• FP8 and BF16 serving and fine-tuning
• FP64 HPC and scientific simulation

For these offtake customers, B200 delivers comparable or better results at the lower rental index covered below.

Power, Cooling and Facility Needs

Both chips ship in air-cooled and liquid-cooled configurations, so the server model you order sets the facility plan. NVIDIA's DGX B300 datasheet lists the system as air-cooled, and the DGX B300 user guide describes rear-accessible fans with 1,350–1,500 CFM of airflow. The same guide lists 15 kW system max and 14.5 kW operational, against ~14.3 kW max on the DGX B200 datasheet.

OEM designs vary within each chip. In Dell's InfoHub test, the XE9780 (B200) is air-cooled, and the XE9780L (B300) is direct liquid cooled. The XE9780L needs a coolant distribution unit (CDU), the equipment that moves coolant between the servers and the facility water loop. On the B200 side, Supermicro's HGX B200 systems come in 4U liquid-cooled and 8U air-cooled versions.

Confirm four items with your colocation provider before you order:

• Power per rack. Check that the rack supplies about 15 kW per DGX-class 8-GPU system, plus networking.
• Cooling type. Match air, direct liquid or facility water to the server model on the purchase order.
• CDU availability. Direct-liquid-cooled servers such as Dell's XE9780L need CDU capacity on site.
• Network fabric. B300 doubles networking bandwidth per 8-GPU system, 1.6 TB/s against 0.8 TB/s, per NVIDIA's HGX table, so switches and optics must match.

These items also carry into financing, because the colocation agreement and power and cooling readiness show the servers can be installed and earning.

Rental Prices and Financing: the Operator's Math

Silicon Data's B300 index put B300 at $7.68 per GPU-hour on November 10, 2026, about 10% above B200's $6.97. The B200 rental index is standardized for machine specs, rental terms, platform performance and geolocation. Read both values as market benchmarks; a provider's quote for a given contract can sit above or below them.

Each value is a single day's reading, and rental prices moved sharply in Q4 2026. They may keep moving, so check the indices on the day you price a contract.

B300 carries 1.5x the memory per 8-GPU system (2.1 TB against 1.4 TB) for a rental index about 10% higher. Whether that spread pays depends on whether the offtake customer needs the memory. A customer serving frontier models at long context uses the headroom. A customer whose model fits in 1.4 TB pays more per GPU-hour for capacity it leaves idle. On the capital side, an 8-GPU B300 server costs about $600,000 at Q4 2026 pricing, server-only and pre-tax, per USD.AI's pricing research.

In our view, from the deals we review, the chip choice alone doesn't change financeability. Both B200 and B300 are on the approved collateral list in USD.AI's underwriting standards (October 2026). Lenders underwrite the offtake contract and the operator alongside the hardware.

A financing review looks at the offtake contract and the customer's credit quality, the operator's track record and the hardware purchase order. It also covers the colocation agreement and power and cooling readiness. An offtake contract matched to the chip is what repays the loan. The USD.AI loan history includes 8 B200 servers in Saint-Denis, France, financed in January 2026.

When to Choose Which

Frontier-model inference at long context: B300. Your offtake customer serves frontier models whose weights plus KV cache exceed 1.4 TB per system. Dell's test on Kimi K2.6, a 600B+ mixture-of-experts model, ran FP4 at 16K input/2K output and concurrency 128. Memory headroom and 2x attention decide this case.

Models that fit, FP8/BF16 serving or FP64 HPC: B200. FP8 is equal at 72 PFLOPS sparse per 8-GPU system. FP64 runs 296 TFLOPS on B200 against 10 TFLOPS on B300. B200 also rents at the lower index.

Air-cooled site without CDU capacity: either chip. NVIDIA's DGX B300 and 8U air-cooled HGX B200 systems both run on air. Check the specific server model before you sign the purchase order.

FAQ

Is the B300 liquid cooled?

Not necessarily: NVIDIA's DGX B300 is air-cooled, and some OEM B300 servers, such as Dell's XE9780L, use direct liquid cooling with a coolant distribution unit. The server model you order sets the cooling requirement, so check it against your colocation site.

Does B300 always outperform B200?

No: in Dell's InfoHub test, B300 trailed B200 by 13–17% on Nemotron NVFP4 at 16K–32K context and concurrency 128. NVIDIA also lists B300 at 10 TFLOPS FP64 per 8-GPU system against 296 TFLOPS on B200. B300's lead appears when workloads are memory-bound at long context.

What is the price difference between B200 and B300?

On Silicon Data's rental indices for November 10, 2026, B300 rented at $7.68 and B200 at $6.97 per GPU-hour, about 10% apart. Both are standardized index values, and rental prices moved sharply in Q4 2026.

Can I finance B300 or B200 servers?

Yes: both chips are on the approved collateral list in USD.AI's underwriting standards, and the review centers on the offtake contract and the operator. Financing through USD.AI covers up to 80% of the deployment cost, with the operator contributing at least 20% as equity.

Conclusion

The b300 vs b200 decision comes down to whether your offtake workload needs more than 1.4 TB per 8-GPU system at long context. Choose B300 for memory-bound frontier inference at long context, and B200 for workloads that fit, FP8 serving and FP64 HPC.

Whichever chip matches the offtake contract, deals financed through USD.AI are scored by Compute IQ on the operator, the customer and the offtake contract. Talk to USD.AI about financing your next B300 or B200 deployment.