NVIDIA B300 vs B200 for AI Inference: Is Blackwell Ultra Worth the Premium? (2026 Cost-Per-Token Guide)

B300 (Blackwell Ultra) shipped January 2026. B200 has been in production since mid-2025 and has a mature software stack. So the question now isn't "when will B300 be available?", it's whether the B300 premium pays off for your specific workload. The answer depends on model size, batch size, and whether your serving stack supports FP4.

This post works through the real numbers: specs, throughput benchmarks, live cost-per-token math, and a binary decision checklist.

Quick Answer: B300 vs B200 at a Glance

Both GPUs share the same 8 TB/s memory bandwidth. The B300 also has 55% more FP8 compute (7,000 vs 4,500 TFLOPS) and 56% more FP16 throughput (3,500 vs 2,250 TFLOPS), along with 67% more FP4 throughput (15,000 vs 9,000 TFLOPS) and 96 GB more VRAM.

Both are available as spot and dedicated instances on Spheron.

Where the Extra VRAM and FP4 Compute Actually Matter

Memory-bound vs compute-bound: how to tell which one you have

A workload is memory-bandwidth-bound when the GPU's tensor cores sit idle waiting for data from VRAM. It's compute-bound when the tensor cores are saturated and data movement isn't the bottleneck. For LLM inference, the line between these two regimes depends on model size and batch size together.

Small models at low batch sizes are almost always memory-bandwidth-bound. The GPU loads weight matrices from VRAM faster than it can accumulate enough requests to fill the tensor cores. Large models at large batch sizes become compute-bound as the matrix multiplications grow large enough to saturate the FP4 units. A practical test: if doubling your batch size roughly halves your cost-per-token, you're bandwidth-bound. If it doesn't, you're entering compute-bound territory.

For a detailed cross-GPU cost-per-token benchmark covering A100, H100, H200, and B200 models at various batch sizes, see our GPU cost-per-token benchmark guide.

Long-context workloads and KV cache

Both the B300 and B200 run at 8 TB/s, so raw decoding speed at small batch is similar when models fit in memory with headroom. Where B300 pulls ahead is VRAM capacity for KV cache. At 32K+ context windows or high concurrency, the B200's 192 GB starts evicting KV cache blocks to CPU memory via paged attention's eviction policy, which increases tail latency as hot blocks get swapped back. The B300's 288 GB defers that eviction pressure with 96 GB more headroom.

For a 70B FP16 model (140 GB weights) at batch size 32, the B200 has about 52 GB left for KV cache. A single forward pass of Llama 3 70B at 32K context per request can consume 8-16 GB of KV cache depending on the number of heads and layers. At 32 concurrent users you hit the ceiling fast. The B300 gives you 148 GB of KV cache headroom under the same conditions.

Large-batch inference and FP4 compute

At batch sizes where the workload becomes compute-bound, the B300's 67% FP4 advantage translates directly to 67% more tokens per second. For a high-traffic API serving 100+ concurrent users on a 70B model, this means running fewer B300s than B200s for the same throughput target. Cost-per-token breaks in favor of B300 once you're in this regime.

The compute advantage only kicks in with FP4-compatible serving. TensorRT-LLM 0.15+ and vLLM with FP4 support are required. Both tools have been FP4-capable since Q1 2026, so this is not a future-roadmap requirement; it's a configuration step.

MoE models

Mixture-of-Experts models like DeepSeek V3 and Qwen 3 MoE have high total parameter counts but sparse active-parameter density per token. They're memory-capacity-bound before they're compute-bound because the full expert weight matrices must reside in VRAM even if only a fraction activates per forward pass. For MoE models that exceed B200's 192 GB, the B300 with 288 GB is the only single-GPU option.

Full Spec Head-to-Head

All TFLOPS values are dense (non-sparse). NVIDIA publishes 2:4 structured-sparsity figures that are roughly 2x higher but require sparse weight patterns. The dense figures represent realistic production throughput for general inference workloads.

For a full B300 technical breakdown including infrastructure requirements and DGX vs HGX configurations, see our NVIDIA B300 Blackwell Ultra guide. For B200 deep-dive specs and historical pricing, see the NVIDIA B200 complete guide.

Inference Benchmarks and Cost-Per-Token

The table below uses throughput estimates based on published MLPerf data and architectural scaling ratios, with live Spheron pricing from the API.

Cost/1M tokens formula: ($/hr) / (tok/s × 3600 / 1,000,000). For multi-GPU rows, the $/hr reflects the full cluster cost (GPU count times per-GPU spot price), not the per-GPU rate.

The 405B row shows the memory-fit advantage of B300. Two B300s (2 × 288 GB = 576 GB total) hold the full 405B model at FP8, where three B200s are needed (3 × 192 GB = 576 GB). Saving one GPU reduces hardware overhead and lowers the hourly cluster cost from $8.04 to $6.58. However, the 2-GPU B300 cluster has less total memory bandwidth (2×8 TB/s = 16 TB/s) than the 3-GPU B200 cluster (3×8 TB/s = 24 TB/s). For a 405B model at FP8, inference is memory-bandwidth-bound at typical batch sizes, so throughput scales primarily with total bandwidth. The 50% bandwidth advantage of the 3-GPU configuration explains why 3x B200 delivers higher tokens per second and better cost-per-token for throughput-intensive workloads. 2x B300 is the right call when minimizing GPU slot count matters more than maximizing per-token throughput.

Pricing fluctuates based on GPU availability. The prices above are based on 10 Jun 2026 and may have changed. Check current GPU pricing → for live rates.

When B300 Pays for Itself

At current spot pricing, a single B300 costs $3.29/hr versus $2.68/hr for a B200, a $0.61/hr premium. Running 24/7, that's $14.64 per day more.

For Llama 3.3 70B FP4, the B300 delivers ~16,500 tok/s versus ~10,000 tok/s on B200. The cost-per-token gap is $0.074/M on B200 versus $0.055/M on B300, a saving of $0.019 per million tokens.

To cover the $14.64/day premium purely from lower cost-per-token, you need to be serving approximately 770 million tokens per day on a single GPU. That works out to roughly 9,000 tokens per second sustained throughput, or about 90% of B200's single-GPU capacity for 70B FP4.

In practice, you're not running a single GPU at 90% utilization before you add capacity. The real break-even for teams scaling a fleet is lower: once you'd otherwise add a second B200 to handle demand, the math often favors one B300 instead.

For the full FP4 throughput breakdown on Blackwell including quality tradeoffs and framework setup, see our guide on FP4 quantization on Blackwell GPUs.

When B200 (or H200) Is the Smarter Rent

Models fitting in 192 GB with VRAM headroom. For 7B through 70B models at FP8 or FP4, the B200's 192 GB is sufficient and its lower price wins on cost-per-token. No VRAM advantage from upgrading to B300, and both share the same 8 TB/s bandwidth.

FP16-only serving stacks. If your framework doesn't support FP4, the B300's extra FP4 compute does nothing. Although B300 has 55% more FP8 compute (7,000 vs 4,500 TFLOPS), most inference workloads at small-to-medium batch sizes are memory-bandwidth-bound, not compute-bound. Both GPUs share the same 8 TB/s bandwidth, which is the actual bottleneck in this regime, so the extra compute doesn't translate to higher throughput. At $3.29/hr versus $2.68/hr, B300 costs 23% more for effectively the same tokens per second on FP8/FP16 stacks at typical batch sizes, making B200 the clear choice.

Sub-70B inference at low concurrency. Memory-bandwidth-bound workloads don't benefit from additional compute. The bottleneck is 8 TB/s, which both GPUs share. For small models at low batch sizes, B200 and B300 will deliver similar tokens per second, so B200 wins on cost.

Fine-tuning 70B models. QLoRA on a single B200 (192 GB) gives ample VRAM headroom for the base model plus optimizer states. The B300 premium is not justified for single-run training jobs where VRAM isn't the constraint.

For B200 pricing and availability, explore B200 on Spheron. For a three-way comparison that includes GB200, see the H200 vs B200 vs GB200 guide. H200 is also worth considering when neither Blackwell GPU is needed, with pricing starting lower than both.

Availability and Pricing Reality in Mid-2026

Both B300 and B200 are available on Spheron today as spot and dedicated instances. Spot pricing gives access to hardware at reduced rates for interruptible jobs. Dedicated instances guarantee availability for production inference SLAs.

Spheron aggregates compute from 5+ providers, which is why pricing reflects real competition rather than a single provider's margin structure. The lowest available rate for B300 spot starts at $3.29/hr per GPU (8-GPU bundle configuration). B200 spot starts at $2.68/hr per GPU.

Pricing trajectory follows the H100 pattern: as B300 supply scales across the Spheron provider pool, rates will compress. The H100 dropped from $8+/hr in early 2024 to roughly $3/hr spot by Q1 2026. B300 is already at roughly $3.29/hr spot, down from $6.80/hr at launch. If you're evaluating long-term contracts, per-minute billing on spot is the better model for workloads with unpredictable utilization.

Decision Checklist: Which Blackwell SKU to Rent

Does your model exceed 192 GB in VRAM?

Yes: B300 is your only single-GPU option. Rent B300 on Spheron →

No: Continue.

Does your model require more than 128 GB of VRAM (for example, 70B FP16 at ~140 GB)?

Yes: B200 fits it; B300 gives more KV cache headroom at scale. Both are viable.

No: H200 handles it at lower cost.

Is your inference stack FP4-capable (TensorRT-LLM 0.15+, vLLM FP4)?

Yes: B300 delivers 67% more FP4 throughput. Run the break-even math above.

No: Both GPUs share 8 TB/s bandwidth, so FP8 throughput is similar for bandwidth-bound workloads at typical batch sizes. B200 wins on cost.

Are you serving 100+ concurrent users on a 70B model?

Yes: Compute-bound territory at high batch sizes. B300 reduces cost-per-token at scale.

No: Likely bandwidth-bound at low concurrency. B200 is enough.

Is guaranteed availability required for a production SLA?

Yes: Use B300 or B200 dedicated instances (no spot interruption risk).

No: B300 or B200 spot for the lowest hourly rate.

Both B300 and B200 are available on Spheron as spot and dedicated instances. Benchmark your actual workload on real hardware before committing to a migration path.

B300 on Spheron → | B200 GPU pricing → | View all pricing →