Blog ▸ B300 vs B200: Price, Availability and Which One to Commit To in Q4 2026
GPU Infrastructure
The newer part has 50% more memory, more FP4, and this quarter it costs less on committed terms. Here is the one workload where B200 still makes sense, and the catch on B300.
B300 vs B200: Price, Availability and Which One to Commit To in Q4 2026
GPUaaS.com Team
GPU Infrastructure
October 6, 2026
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On committed terms in Q4 2026, B300 clusters quote at $5.00 to $6.50 per GPU-hour against $7.00 to $8.00 for B200, and B300 carries 288GB of HBM3e against 192GB, so for most training and inference workloads B300 is the part to commit to this quarter.
That is not how a generational comparison is supposed to come out, and it will not stay that way. This post works through the spec delta, the price delta from the October benchmark, where each part is actually available, and the one class of workload where B200 is still the right answer.
Key takeaways
B300 committed band is $5.00 to $6.50 per GPU-hour this month; B200 is $7.00 to $8.00. The newer part is cheaper at both ends
B300 carries 288GB HBM3e per GPU against 192GB on B200, with dense FP4 of roughly 14 to 15 PFLOPS against about 9
B300 has essentially no FP64 (around 1.25 TFLOPS against 37 on B200), which rules it out for double-precision HPC
On-demand B300 hit a record $7 to $10 per GPU-hour in mid-October, so the committed discount on B300 is wider than on any other part
B300 nodes draw more power (up to 1,100W to 1,400W per GPU depending on the system) and need liquid cooling at HGX density, or 14.5 kW of air per node on DGX
Blackwell node lead times to buy run 16 to 26 weeks; committed rental on either part provisions in days to weeks depending on region
◆ B300 VS B200: THE NUMBERS THAT CHANGE THE DECISION
Spec
B300 (Blackwell Ultra)
B200 (Blackwell)
Delta
HBM3e per GPU
288GB (12-high stacks)
192GB (180GB usable on some SKUs)
+50%
Memory bandwidth
About 8 TB/s
About 8 TB/s
Level
Dense FP4
About 14 to 15 PFLOPS
About 9 PFLOPS
+55% to +67%
Dense FP8
About 10 PFLOPS
About 9 PFLOPS
+10%
FP64
About 1.25 TFLOPS
37 TFLOPS
B300 drops it
NVLink per GPU
1.8 TB/s
1.8 TB/s
Level
Node NIC
ConnectX-8, 800G
ConnectX-7, 400G
2x inter-node
TDP per GPU
Up to 1,100W (HGX) to 1,400W
Up to 1,000W
+10% to +40%
Cooling
Liquid at HGX density; DGX is air-cooled at 14.5 kW
Liquid for dense, air possible
B200 easier to site
Committed band, Oct 2026
$5.00 - $6.50 / GPU-hr
$7.00 - $8.00 / GPU-hr
B300 lower
NVIDIA datasheets and system vendor specs as compiled by DataCrunch, Spheron and VESSL, 2026. Committed bands from the GPUaaS.com October 2026 benchmark. FP4 and FP8 figures are dense, not sparse.
◆ THE PRICE DELTA
Why the newer part is cheaper this quarter
Three things are compressing the B300 band at the same time. Supply is arriving: Blackwell Ultra allocation that was promised for the second half of the year has landed with several partners in the network, and committed capacity that is live gets quoted rather than held. B200 fleets, by contrast, were bought earlier at higher unit cost and their owners are still pricing to recover it.
The second is that B200 demand is sticky. Specs written in the first half of 2026 named B200 because B300 was not available, and a lot of those specs have not been revisited. Providers with B200 racks know that and hold the band.
The third is the on-demand reference. Compute Desk's index put on-demand B300 at a record $7 to $10 per GPU-hour by 15 October, with Supermicro build capacity named as the constraint. When the on-demand number sits that far above the committed band, buyers who can commit do, and the committed B300 band fills fast enough that it has not needed to rise. The two-price market covers why on-demand and committed can diverge that far on the same silicon.
$1.50
the gap, per GPU-hour, between the bottom of the B200 band and the bottom of the B300 band this month, in favour of the part with 50% more memory
GPUaaS.com October 2026 benchmark
◆ THE SPEC DELTA
Memory is the headline, FP4 is the real gap, FP64 is the catch
The 288GB figure gets the attention because it is simple, and it matters: a 405B-parameter model in FP8 fits on fewer B300 GPUs than B200, and a long-context KV cache that spills on 192GB stays resident on 288GB. Fewer partitions means less all-reduce traffic, which on a multi-node job shows up directly in step time.
The bigger compute gap is in FP4. Dense FP4 on B300 is roughly 14 to 15 PFLOPS per GPU against about 9 on B200, a 55% to 67% uplift depending on whose datasheet you read. FP8 moves only about 10%. So the B300 advantage is largest for inference stacks already running FP4 quantisation on Blackwell, and smallest for training in BF16 where the two parts are closer than the marketing suggests.
The catch is FP64. Blackwell Ultra traded almost all of its double-precision throughput for the FP4 uplift, down to around 1.25 TFLOPS from 37 on B200. For AI workloads that is irrelevant. For CFD, molecular dynamics, weather, or any scientific code that lives in FP64, B300 is the wrong part at any price, and B200 or even H100 is the right one.
◆ AVAILABILITY BY REGION
Where each part can actually be sourced
North America has the deepest B300 supply in the network this month, and B200 is widely available there too. Europe has B300 allocation arriving but it is the region with the most unfilled requests, driven by residency requirements that rule out capacity in other jurisdictions. Scaleway took the first EU B300 allocation earlier this year and new European entrants are buying Blackwell Ultra specifically for Dublin, Helsinki and the Nordics.
APAC is better than most buyers assume on B300, with capacity in Japan, Singapore and India sourceable on committed terms. The Middle East has announced capacity that outruns rentable capacity on both parts, and sovereign programs there tend to specify GB300 rack-scale rather than HGX nodes.
Regional price variation was minimal this month across the whole benchmark, so the region question is about lead time and residency, not rate. A request that can accept capacity in any region outside China and Taiwan fills fastest. One that names a single country fills on the timeline that country's supply allows.
◆ THE BREAKEVEN
When B200 still wins
B200 wins in three situations. The first is FP64, covered above. The second is facilities: a B300 node draws more power, and while the DGX B300 is air-cooled it pulls 14.5 kW in one 10U chassis, so a buyer whose colocation contract or owned hall is air-cooled at under 40 kW a rack can site B200 comfortably and B300 only one or two to a rack. That matters for owned hardware more than rentals, but it also narrows which providers can host B300 at all.
The third is a workload that is compute-bound in BF16 or FP8 rather than memory-bound, running on a stack that is not yet using FP4. There the B300 uplift is about 10%, and at this month's bands B300 is still cheaper, so even then B300 wins on price. The honest version is that at $5.00 to $6.50 against $7.00 to $8.00, B200 only wins on price when a buyer has an existing B200 commitment with months to run, and the question is whether to extend it or move.
The sizing arithmetic for that is in B300 cluster sizing and the measured inference numbers in B300 cost per million tokens. For a buyer who was on H200 and is skipping B200 entirely, H200 vs B200 is the older comparison and most of its B200 conclusions now transfer to B300 at a lower price.
◆ WHICH PART FOR WHICH WORKLOAD, Q4 2026
Workload
Pick
Why
Frontier or large-model pretraining, multi-node
B300
288GB cuts partition count; FP4 for mixed-precision stacks; cheaper band
Long-context inference (128K+), reasoning models
B300
KV cache stays resident; FP4 throughput; cheaper band
Fine-tuning 70B to 405B
B300
Fewer GPUs per job at the lower band
BF16 training, FP4 not in the stack
B300, narrowly
Only a 10% compute gap, but B300 is still the cheaper band
Extending an existing B200 commitment
Depends on months left
Migration cost against the band gap; run the numbers
FP64 scientific compute
B200 or H100
B300 has effectively no double precision
Air-cooled facility, owned hardware
B200
B300 needs liquid cooling at HGX density
GPUaaS.com guidance, October 2026. Bands move monthly; confirm with a current quote before locking a spec.
◆ HOW TO COMMIT
What to put in the request
Specify B300 unless FP64 or facility cooling rules it out. Give the term length first, because it moves the quote more than anything else: twelve months lands at the bottom of the band, three months at the top. Say whether the allocation needs to be contiguous and InfiniBand-connected, since non-contiguous Ethernet capacity prices lower and suits inference fine.
Name the region only if residency requires it. If it does, say which jurisdiction and why, because that moves the request into a shorter queue of providers who can satisfy it. If it does not, leave it open and let the network source where the lead time is shortest.
Quotes come back within a few hours from 20+ vetted partners, with no buyer fees, and the contract runs between the provider, GPUaaS.com and the buyer. For single B200 or B300 GPUs rather than an 8-GPU node minimum, packet.ai handles self-serve access.
Get a B300 or B200 cluster quote inside this month's bands.
Submit a spec and quotes come back within a few hours, sourced across 20+ vetted partners. No buyer fees. 8-GPU node minimum.
On committed terms this month, yes. The B300 band is $5.00 to $6.50 per GPU-hour against $7.00 to $8.00 for B200 across the GPUaaS.com network. On-demand is the reverse: B300 on-demand hit a record $7 to $10 in mid-October, so the committed discount on B300 is unusually wide.
B300 (Blackwell Ultra) carries 288GB HBM3e against 192GB on B200, roughly 55% to 67% more dense FP4 compute, 800G ConnectX-8 networking against 400G, and a higher TDP that needs liquid cooling at HGX density or 14.5 kW of air per DGX node. It drops almost all FP64 throughput, from 37 TFLOPS to around 1.25, so it is unsuitable for double-precision HPC.
B300. The 288GB per GPU keeps long-context KV caches resident, the FP4 uplift is where the compute gap is largest, and the committed band is lower this month. B200 only makes sense for inference if you already hold a B200 commitment with significant time left to run.
For FP64 scientific workloads, where B300 has effectively no double precision. For owned hardware in air-cooled facilities, since B300 at HGX density needs direct liquid cooling. And when an existing B200 contract has months remaining and migration cost outweighs the band gap.
North America has the deepest committed B300 supply in the GPUaaS.com network this month, APAC is better supplied than most buyers assume, and Europe has allocation arriving but the most unfilled requests because of residency requirements. Regional price variation is minimal; the difference is lead time.
Buying a Blackwell node runs 16 to 26 weeks from order to delivery. Committed rental through GPUaaS.com returns quotes within a few hours and provisions in days to weeks depending on region and whether the capacity is already live or presold against a future date.
