Trillion-Parameter MoE Models with Open Weights: Quantized Storage and Deployment in Practice

1. Open Weights Reach Trillion Scale: A Watershed in Engineering Paradigm

Open-weight models have been closing in on closed-source frontiers for the past two years, but public cases that simultaneously deliver “trillion-level total parameters, million-token context, and fully obtainable weights” remain rare. A recent ultra-large MoE model released its complete weights along with a model card disclosing architecture parameters, head-to-head evaluation results against six mainstream models, and the full text of a custom license.

Its disclosed specifications can be summarized in four points:

• Total parameters of approximately 2.8 trillion (2.8T);
• About 104 billion activated parameters per token (roughly 104B), only around 3.7% of the total;
• A context window of 1 million tokens;
• Weights released in MXFP4 low-precision quantized format, at approximately 1.56TB.

For developers and enterprise teams, what matters is not the size of the numbers but three things: capabilities can be privatized (complete weights, open to further fine-tuning), cost is controllable (inference compute is determined by activated parameters), and compliance can be front-loaded (license and evaluations disclosed alongside the weights, enabling legal review in advance). Each is unpacked below.

2. The MoE Engineering Ledger: Total and Activated Parameters Must Be Read Separately

The core idea of MoE (Mixture of Experts) is conditional computation: the model consists of many parallel expert networks, and a router selects only a few experts per token to participate in computation. Thus, the model’s “knowledge capacity” is determined by total parameters, while “per-token compute” is determined by activated parameters. A simplified illustration:

import torch
import torch.nn as nn
import torch.nn.functional as F
class MoERouter(nn.Module):
    """Simplified illustration: each token is routed to only top_k experts"""
    def __init__(self, hidden_dim, n_experts, top_k):
        super().__init__()
        self.gate = nn.Linear(hidden_dim, n_experts)
        self.top_k = top_k
    def forward(self, x):                       # x: [n_tokens, hidden_dim]
        logits = self.gate(x)                   # [n_tokens, n_experts]
        probs = F.softmax(logits, dim=-1)
        top_p, top_i = torch.topk(probs, self.top_k, dim=-1)
        top_p = top_p / top_p.sum(-1, keepdim=True)   # renormalize weights
        return top_i, top_p                     # only these top_k experts are activated

Using the case above as a reference: 2.8 trillion total parameters provide knowledge capacity, while each token activates only about 104 billion parameters. Roughly speaking, per-token inference compute is on the same order as the activated parameters, so its cost is closer to a hundred-billion-scale dense model than to a 2.8-trillion dense model—this is precisely the structural advantage of “trillion-level capacity at hundred-billion-level cost.”

But MoE also carries two engineering costs that must be confronted:

1. Memory is still accounted against total parameters. No matter how few are activated, all expert weights must reside in GPU memory or host memory, which directly drives the quantization requirement discussed below.
2. Communication and load balancing. With expert parallelism, tokens must be routed across nodes to the corresponding experts; All-to-All communication overhead and skewed expert load are the main bottlenecks, and both training and inference require load-balancing losses and scheduling optimization.

The 1-million-token context is another ledger entry. With a typical configuration as a rough estimate (illustrative: 80 layers, 8 KV heads, head dimension 128, FP16 storage), the KV cache per token is approximately 2×80×8×128×2 ≈ 0.31MB, so a single sequence at full context reaches on the order of 300GB of KV cache. This means long-context scenarios must rely on paged attention, low-precision KV cache storage, prefix cache reuse, and similar techniques; otherwise memory will be exhausted by the KV cache rather than the weights.

3. MXFP4 Quantization: The Storage Ledger from 5.6TB to 1.56TB

First, the arithmetic. Storing 2.8 trillion parameters in FP16:

FP16:  2.8e12 params × 2 Byte = 5.6 TB
MXFP4: 4 bit weights + one 1-Byte scale factor shared per 32 elements
       ≈ 2.8e12 × (0.5 + 1/32) Byte ≈ 1.49 TB

MXFP4 belongs to the “microscaling” format family: unlike naively compressing weights to 4 bits, it shares one exponent scale per small block (typically 32 elements), effectively preserving an independent dynamic range for each small group of weights. This alleviates the overflow or underflow that naive low-bit formats suffer when the numerical distribution span is large, making 4-bit deployment of ultra-large weights feasible with controlled precision loss.

An interesting arithmetic detail: pure 4-bit plus scale factors theoretically comes to about 1.49TB, while the actual release is about 1.56TB. The extra tens of GB typically come from the industry-common mixed-precision strategy—retaining higher precision for quantization-sensitive modules such as embeddings, shared layers, and normalization. This is essentially an explicit “precision versus size” engineering trade-off.

The implications for deployment are direct:

• A single 8-GPU server with about 80GB of memory per GPU offers roughly 640GB of usable memory. 1.56TB of weights requires deployment across at least about 3 such machines, with room still needed for KV cache and activations; sticking with FP16 would require roughly 9 machines for weights alone.
• Weight distribution itself becomes an engineering problem: distributing 1.56TB from object storage to multiple nodes requires sharding, checksum verification, nearby caching, and incremental pull mechanisms; otherwise every scale-out is an hours-long wait.

4. Post-Training Reinforcement Learning: The Main Engine from “Knowing” to “Doing”

One detail worth magnifying in the disclosed information: the underlying architecture continues from the previous generation with total parameters unchanged, and capability gains come mainly from increased investment in reinforcement learning during post-training. This confirms a mainstream industry understanding:

• Pretraining determines the knowledge ceiling; post-training determines behavioral quality;
• For agent scenarios, capabilities such as multi-turn tool calling, long-horizon planning, and error recovery are hard to teach through supervised fine-tuning alone—high-quality multi-turn trajectories are too scarce—whereas reinforcement learning can use “whether the task was ultimately completed” directly as a reward signal, letting the model trial-and-error in real interaction.

A typical post-training pipeline looks roughly like this:

SFT (instruction and format alignment)
  → preference alignment (preference optimization based on human or model feedback)
  → reinforcement learning with verifiable rewards (executable code, checkable math, acceptance-testable tasks)
  → multi-turn agent reinforcement learning (tool-calling loops, long-horizon task rewards)

“Verifiable rewards” is the key design: anchoring rewards to automatically judgeable outcomes—whether unit tests pass, whether retrieval results hit the question, whether files are correctly generated. Only when reward signals can be automated can reinforcement learning scale. This also explains why “with the same parameter scale, capability gaps between two generations can be large”: post-training recipe and compute investment have become variables as important as architecture.

5. Evaluation Methodology: Why “17,500+ Real Long-Horizon Sessions” Is More Credible Than Static Benchmarks

A third-party agent evaluation leaderboard disclosed around the same time offers another valuable data point: its ranking is based on more than 17,500 real users’ long-horizon agent sessions, covering work, coding, and chat tasks. In terms of results, the model (reasoning-enhanced version) ranks first among open-weight models and 13th among all evaluated models; its net improvement rate is +5.1%, versus only +0.9% for the previous generation.

“Net improvement rate” can be understood as: in blind comparisons, the net share of users’ positive preference for the new version minus negative preference. Expressed in minimal pseudocode:

def net_improvement(old, new, sessions):
    win = lose = tie = 0
    for s in sessions:                      # real user long-horizon sessions
        pref = blind_pairwise(old, new, s)  # anonymous two-model blind test
        if pref == "new":   win  += 1
        elif pref == "old": lose += 1
        else:               tie  += 1
    return (win - lose) / (win + lose + tie)   # net improvement rate

The shift from +0.9% to +5.1% means user-perceptible progress is significantly greater than the previous generation’s iteration, which is more persuasive than any static score.

Why emphasize “real users, long-horizon sessions”? Because static benchmarks have three well-known failure modes: first, data contamination, where test questions leak into pretraining corpora and inflate scores; second, saturation and distortion, since static questions are mostly single-turn with short context and cannot assess multi-turn error correction, tool orchestration, or long-horizon memory; third, distribution mismatch, since real workloads differ from question banks in difficulty distribution, ambiguity of phrasing, and context length.

The enterprise selection lesson: treat public leaderboards as a “first screen” and self-built long-horizon session evaluations as the “final review.” Maintain a continuously updated internal task set (desensitized real tickets, code and document tasks), and use blind testing, A/B experiments, and model-assisted review (with human spot checks) to continuously track candidate models’ net improvement rate rather than relying on one-off benchmark runs.

6. Private Deployment Reference Architecture and Self-Check Checklist

Assuming “trillion-parameter MoE + million-token context + agent workloads,” the private deployment chain can be divided into five layers:

1. Weight storage and distribution layer: about 1.56TB of weights hosted in object storage, sharded by layer or by expert, with hash verification and nearby caching, supporting incremental node pulls.
2. Inference compute layer: a multi-machine, multi-GPU cluster using a hybrid split of tensor parallelism and expert parallelism; inference kernels friendly to low-bit quantized formats are a key selection criterion—weight dequantization should be fused into matrix multiplication to avoid the memory and latency amplification of explicit dequantization.
3. Service orchestration layer: a container platform handles elastic scaling and canary releases of inference instances; for long-context scenarios, enable prefix caching and tiered KV cache (hot data resident in GPU memory, cold data spilled to host memory).
4. Agent runtime layer: tool-calling sandbox, multi-turn session state management, task-level timeouts and retries; the stability of this layer directly determines whether long-horizon sessions can run to completion.
5. Observability layer: centrally collect request traces and tool-call chains via a logging service; monitoring and alerting should cover first-token latency, per-token throughput, expert load skew, and KV cache hit rate—expert skew is a MoE-specific key metric whose deterioration often precedes overall performance degradation.

On cloud platforms such as Baidu AI Cloud, the above layers can be assembled from general services including GPU compute, container platforms, object storage, logging, and monitoring/alerting; fully self-built is also possible, but sufficient engineering effort must be reserved for distributing and verifying quantized weights. A quick pre-deployment self-check checklist:

[ ] Memory budget = weights (~1.56TB) + KV cache (depends on context and concurrency) + framework overhead
[ ] License review: commercial scope, fine-tuning and derivative model terms
[ ] Evaluation baseline: internal task set blind testing, record net improvement rate
[ ] Rollback plan: A/B switch between quantized and higher-precision versions
[ ] Expert parallelism stress test: All-to-All communication latency and load balancing behavior

7. License Review: New Homework in the Era of Custom Licenses

This case uses a custom license rather than a standard open-source one, which is not uncommon for trillion-level open weights. Beyond the excitement of “open weights,” technical teams should work with legal to confirm four questions: whether commercial use is permitted and whether there are scale thresholds; whether continued fine-tuning on the weights and release of derivative models is allowed; whether source attribution stating “trained based on these weights” is required; and the license’s revocability and risk of subsequent term changes. Writing these four points into the selection report is far cheaper than rework after deployment.

Conclusion

Abstracting away the specific product, this case leaves technical teams with three lessons. First, MoE decouples “knowledge capacity” from “compute cost”; trillion parameters are no longer a compute barrier—memory is—so low-bit micro-scaling quantization becomes a standard capability in the trillion era. Second, post-training reinforcement learning has become a capability variable as important as architecture; “same parameters, different behavior” will become increasingly common, and selection should require vendors to disclose their post-training methodology. Third, evaluation is shifting from static benchmarks to net improvement rates over real long-horizon sessions, and the ability to build a sustainable in-house evaluation set is becoming an infrastructure investment related to large models.